Autonomous vehicle software version assessment

Description

FIELD

The present disclosure generally relates to systems and methods for operating, monitoring, assessing, or insuring autonomous or semi-autonomous vehicles.

BACKGROUND

Vehicles are typically operated by a human vehicle operator who controls both steering and motive controls. Operator error, inattention, inexperience, misuse, or distraction leads to many vehicle accidents each year, resulting in injury and damage. Autonomous or semi-autonomous vehicles augment vehicle operators' information or replace vehicle operators' control commands to operate the vehicle in whole or part with computer systems based upon information from sensors within the vehicle.

Vehicle or automobile insurance exists to provide financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability that could arise therefrom. Typically, a customer purchases a vehicle insurance policy for a policy rate having a specified term. In exchange for payments from the insured customer, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid on behalf of the insured over time at periodic intervals. An insurance policy may remain “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy. An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when premium payments are not being paid or if the insured or the insurer cancels the policy.

Premiums may be typically determined based upon a selected level of insurance coverage, location of vehicle operation, vehicle model, and characteristics or demographics of the vehicle operator. The characteristics of a vehicle operator that affect premiums may include age, years operating vehicles of the same class, prior incidents involving vehicle operation, and losses reported by the vehicle operator to the insurer or a previous insurer. Past and current premium determination methods do not, however, account for use of autonomous vehicle operating features. The present embodiments may, inter alia, alleviate this and/or other drawbacks associated with conventional techniques.

BRIEF SUMMARY

The present embodiments may be related to autonomous or semi-autonomous vehicle functionality, including driverless operation, accident avoidance, or collision warning systems. These autonomous vehicle operation features may either assist the vehicle operator to more safely or efficiently operate a vehicle or may take full control of vehicle operation under some or all circumstances. The present embodiments may also facilitate risk assessment and premium determination for vehicle insurance policies covering vehicles with autonomous operation features. For instance, a consumer may opt-in to a rewards program that rewards them, such as in the form of insurance discounts, for affirmatively sharing data related to their vehicles and/or autonomous features with an insurance provider.

In accordance with the described embodiments, the disclosure herein generally addresses systems and methods for updating an autonomous operation feature of an autonomous or semi-autonomous vehicle, which update may include an update to a version of software. A computer (such as an on-board computer, mobile device, or server communicatively connected to the vehicle) associated with the vehicle may (1) receive an indication of an update associated with the autonomous operation feature; (2) identify a plurality of autonomous or semi-autonomous vehicles having the autonomous operation feature; (3) and/or cause the update to be installed within one or more of the identified plurality of autonomous or semi-autonomous vehicles, such as via wireless communication or data transmission of a software update from a remote server to the on-board computer. The indication of the update may include a request to provide the update to the plurality of autonomous or semi-autonomous vehicles having the autonomous operation feature.

In some embodiments, the computer may further (4) determine a change in a risk level associated with one or more of the identified plurality of autonomous or semi-autonomous vehicles corresponding to the update associated with the autonomous operation feature; (5) determine whether each change in the risk level meets one or more criteria for installing the update; and/or (6) cause the update to be installed for only the one or more of the identified plurality of autonomous or semi-autonomous vehicles for which the change in the risk level meets the one or more criteria. The one or more criteria may include a reduction in risk of at least a threshold magnitude, which threshold magnitude may be greater than or equal to zero. A change in one or more costs associated with an insurance policy associated with each of the one or more of the identified plurality of autonomous or semi-autonomous vehicles may further be determined, and the one or more criteria may include a reduction in the one or more costs associated with the insurance policy of at least a threshold magnitude, which threshold magnitude may be greater than or equal to zero.

In further embodiments, the computer may cause a notification regarding the update to be presented to each of one or more insurance customers associated with the identified plurality of autonomous or semi-autonomous vehicles. The notification may include information regarding a cost associated with the update and information regarding a change in one or more costs associated with an insurance policy associated with the one or more of the identified plurality of autonomous or semi-autonomous vehicles. Once the notifications have been presented, the computer may receive an indication of acceptance of the update from the one or more insurance customers. In such embodiments, the update may be installed within one or more of the identified plurality of autonomous or semi-autonomous vehicles by transmitting (such as via wireless communication or data transmission) the update (such as an updated version of software controlling autonomous or semi-autonomous vehicle functionality) to the one or more of the identified plurality of autonomous or semi-autonomous vehicles.

In each of the embodiments or aspects described above, the methods may be provided in corresponding computer systems including at least one or more processors and a non-transitory program memory coupled to the one or more processors and storing executable instructions. The computer systems may further include or be communicatively connected to one or more sensors, communication components or devices, or other equipment as described herein. In yet another aspect, a tangible, non-transitory computer-readable medium storing instructions corresponding to each of the embodiments or aspects described above may be provided. Each of the methods or executable instructions of the computer systems or computer-readable media may include additional, fewer, or alternate actions, including those discussed elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

The figures described below depict various aspects of the applications, methods, and systems disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed applications, systems and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Furthermore, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.

FIG. 1 illustrates a block diagram of an exemplary computer network, a computer server, a mobile device, and an on-board computer for implementing autonomous vehicle operation, monitoring, evaluation, and insurance processes in accordance with the described embodiments;

FIG. 2 illustrates a block diagram of an exemplary on-board computer or mobile device;

FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method in accordance with the presently described embodiments;

FIG. 4 illustrates a flow diagram of an exemplary monitoring method during vehicle operation in accordance with the presently described embodiments;

FIG. 5 illustrates a flow diagram of an exemplary operating status assessment method in accordance with the presently described embodiments;

FIG. 6 illustrates a flow diagram of an exemplary operating status monitoring method in accordance with the presently described embodiments;

FIGS. 7A-B illustrate flow diagrams depicting exemplary vehicle operation hand-off methods in accordance with the presently described embodiments:

FIG. 8 illustrates a flow diagram depicting an exemplary vehicle operator identification method in accordance with the presently described embodiments;

FIG. 9 illustrates a flow diagram depicting an exemplary vehicle operator use monitoring and evaluation method in accordance with the presently described embodiments;

FIG. 10 illustrates a flow diagram depicting an exemplary cost comparison method in accordance with the presently described embodiments; and

FIG. 11 illustrates a flow diagram depicting an exemplary autonomous operation feature update method in accordance with the presently described embodiments;

FIG. 12 illustrates a flow diagram depicting an exemplary autonomous vehicle repair method in accordance with the presently described embodiments; and

FIG. 13 illustrates a flow diagram depicting an exemplary infrastructure communication method in accordance with the presently described embodiments.

DETAILED DESCRIPTION

The systems and methods disclosed herein generally relate to evaluating, monitoring, and managing risks related to the operation of autonomous or semi-autonomous vehicles having autonomous (or semi-autonomous) operation features. The systems and methods further relate to pricing and processing vehicle insurance policies for autonomous or semi-autonomous vehicles. The autonomous operation features may take full control of the vehicle under certain conditions, viz. fully autonomous operation, or the autonomous operation features may assist the vehicle operator in operating the vehicle, viz. partially autonomous operation. Fully autonomous operation features may include systems within the vehicle that pilot the vehicle to a destination with or without a vehicle operator present (e.g., an operating system for a driverless car). Partially autonomous operation features may assist the vehicle operator in limited ways (e.g., automatic braking or collision avoidance systems).

The type and quality of the autonomous operation features may affect the risks related to operating a vehicle, both individually and/or in combination. In addition, configurations and settings of the autonomous operation features may further impact the risks. To account for the effects on such risks, some embodiments evaluate the quality of each autonomous operation feature and/or combination of features. Additional embodiments evaluate the risks associated with the vehicle operator interacting with the autonomous operation features. Further embodiments address the relative risks associated with control of some aspects of vehicle control by the autonomous operation features or by the vehicle operator. Still further embodiments address use of information received or generated by the autonomous operation features to manage risk and/or damage.

Some autonomous operation features may be adapted for use under particular conditions, such as city driving or highway driving. Additionally, the vehicle operator may be able to configure settings relating to the features or may enable or disable the features individually or in groups. For example, the vehicle operator may select a mode of operation for the autonomous or semi-autonomous vehicle, which may adjust settings for one or more autonomous operation features. Therefore, some embodiments monitor use of the autonomous operation features, which may include the settings or levels of feature use during vehicle operation, as well as the selection of features or settings of the autonomous operation features chosen by the vehicle operator.

Information obtained by monitoring feature usage may be used to determine risk levels associated with vehicle operation, either generally or in relation to a vehicle operator. In such situations, total risk may be determined by a weighted combination of the risk levels associated with operation while autonomous operation features are enabled (with relevant settings) and the risk levels associated with operation while autonomous operation features are disabled. For fully autonomous vehicles, settings or configurations relating to vehicle operation may be monitored and used in determining vehicle operating risk.

In addition to use in controlling the vehicle, information regarding the risks associated with vehicle operation with and without the autonomous operation features may then be used to determine risk categories or premiums for a vehicle insurance policy covering a vehicle with autonomous operation features. Risk category or price may be determined based upon factors relating to the evaluated effectiveness of the autonomous vehicle features. The risk or price determination may also include traditional factors, such as location, vehicle type, and level of vehicle use. For fully autonomous vehicles, factors relating to vehicle operators may be excluded entirely. For partially autonomous vehicles, factors relating to vehicle operators may be reduced in proportion to the evaluated effectiveness and monitored usage levels of the autonomous operation features. For vehicles with autonomous communication features that obtain information from external sources (e.g., other vehicles or infrastructure), the risk level and/or price determination may also include an assessment of the availability of external sources of information. Location and/or timing of vehicle use may thus be monitored and/or weighted to determine the risk associated with operation of the vehicle.

Autonomous Automobile Insurance

The present embodiments may relate to assessing and pricing insurance based upon autonomous (or semi-autonomous) functionality of a vehicle, utilization of the autonomous (or semi-autonomous) functionality of the vehicle, and/or operation of the vehicle by a human operator. In some embodiments, the vehicle operator may not control the operations of the vehicle directly, in which case the assessment, rating, and pricing of insurance may exclude consideration of the vehicle operator. A smart vehicle may maneuver itself without human intervention and/or include sensors, processors, computer instructions, and/or other components that may perform or direct certain actions conventionally performed by a human operator.

An analysis of how artificial intelligence facilitates avoiding accidents and/or mitigates the severity of accidents may be used to build a database and/or model of risk assessment. After which, automobile insurance risk and/or premiums (as well as insurance discounts, rewards, and/or points) may be adjusted based upon autonomous or semi-autonomous vehicle functionality, such as by groups of autonomous or semi-autonomous functionality or individual features. In one aspect, an evaluation may be performed on how artificial intelligence, and the usage thereof, impacts automobile accidents and/or automobile insurance claims.

The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality. Additionally or alternatively, the autonomous or semi-autonomous functionality or technology may include and/or may be related to: (t) driver alertness or responsive monitoring; (u) pedestrian detection; (v) artificial intelligence and/or back-up systems; (w) navigation or GPS-related systems; (x) security and/or anti-hacking measures; and/or (y) theft prevention systems.

The adjustments to automobile insurance rates or premiums based upon the autonomous or semi-autonomous vehicle-related functionality or technology may take into account the impact of such functionality or technology on the likelihood of a vehicle accident or collision occurring. For instance, a processor may analyze historical accident information and/or test data involving vehicles having autonomous or semi-autonomous functionality. Factors that may be analyzed and/or accounted for that are related to insurance risk, accident information, or test data may include (1) point of impact; (2) type of road; (3) time of day; (4) weather conditions; (5) road construction; (6) type/length of trip; (7) vehicle style; (8) level of pedestrian traffic; (9) level of vehicle congestion; (10) atypical situations (such as manual traffic signaling); (11) availability of internet connection for the vehicle; and/or other factors. These types of factors may also be weighted according to historical accident information, predicted accidents, vehicle trends, test data, and/or other considerations.

In one aspect, the benefit of one or more autonomous or semi-autonomous functionalities or capabilities may be determined, weighted, and/or otherwise characterized. For instance, the benefit of certain autonomous or semi-autonomous functionality may be substantially greater in city or congested traffic, as compared to open road or country driving traffic. Additionally or alternatively, certain autonomous or semi-autonomous functionality may only work effectively below a certain speed, e.g., during city driving or driving in congestion. Other autonomous or semi-autonomous functionality may operate more effectively on the highway and away from city traffic, such as cruise control. Further individual autonomous or semi-autonomous functionality may be impacted by weather, such as rain or snow, and/or time of day (day light versus night). As an example, fully automatic or semi-automatic lane detection warnings may be impacted by rain, snow, ice, and/or the amount of sunlight (all of which may impact the imaging or visibility of lane markings painted onto a road surface, and/or road markers or street signs).

Automobile insurance premiums, rates, discounts, rewards, refunds, points, or other costs may be adjusted based upon the percentage of time or vehicle usage that the vehicle is the driver, i.e., the amount of time a specific driver uses each type of autonomous (or even semi-autonomous) vehicle functionality. Such premiums, rates, discounts, rewards, refunds, points, or other costs may further be adjusted based upon the extent of use of the autonomous operation features, including settings or modes impacting the operation of the autonomous operation features. Moreover, information regarding the vehicle environment during use (e.g., weather, traffic, time of day, etc.) may be included in insurance adjustment determinations, as may traditional information regarding one or more vehicle operators (and the extent to which each vehicle operator uses the vehicle).

Such usage information for a particular vehicle may be gathered over time and/or via remote wireless communication with the vehicle. One embodiment may involve a processor on the vehicle, such as within a vehicle control system or dashboard, monitoring in real-time the vehicle operator and/or the use of autonomous operation features while the vehicle is operating. Other types of monitoring may be remotely performed, such as via wireless communication between the vehicle and a remote server, or wireless communication between a vehicle-mounted dedicated device (that is configured to gather autonomous or semi-autonomous functionality usage information) and a remote server.

Additionally, in some embodiments, the vehicle may transmit and/or receive communications to or from external sources, such as other vehicles (V2V), infrastructure (e.g., a bridge, traffic light, railroad crossing, toll both, marker, sign, or other equipment along the side of a road or highway), pedestrians, databases, or other information sources external to the vehicle. Such communication may allow the vehicle to obtain information regarding other vehicles, obstacles, road conditions, or environmental conditions that could not be detected by sensors disposed within the vehicle. For example, V2V communication may allow a vehicle to identify other vehicles approaching an intersection even when the direct line between the vehicle and the other vehicles is obscured by buildings. As another example, the V2V wireless communication from a first vehicle to a second vehicle (following the first vehicle) may indicate that the first vehicle is braking, which may include the degree to which the vehicle is braking. In response, the second vehicle may automatically or autonomously brake in advance of detecting the deceleration of the first vehicle based upon sensor data.

Insurance premiums, rates, ratings, discounts, rewards, special offers, points, programs, refunds, claims, claim amounts, or other costs associated with an insurance policy may be adjusted for, or may otherwise take into account, the foregoing functionality and/or the other functionality described herein. For instance, insurance policies may be updated based upon installed autonomous operation features, the extent of use of the autonomous operation features, V2V wireless communication, and/or vehicle-to-infrastructure or infrastructure-to-vehicle wireless communication. The present embodiments may assess and price insurance risks at least in part based upon autonomous operation features that replace some actions of the vehicle operator in controlling the vehicle, including settings and operating status of the autonomous operation features.

Exemplary Autonomous Vehicle Operation System

FIG. 1 illustrates a block diagram of an exemplary autonomous vehicle insurance system 100 on which the exemplary methods described herein may be implemented. The high-level architecture includes both hardware and software applications, as well as various data communications channels for communicating data between the various hardware and software components. The autonomous vehicle insurance system 100 may be roughly divided into front-end components 102 and back-end components 104. The front-end components 102 may obtain information regarding a vehicle 108 (e.g., a car, truck, motorcycle, etc.) and the surrounding environment. An on-board computer 114 may utilize this information to operate the vehicle 108 according to an autonomous operation feature or to assist the vehicle operator in operating the vehicle 108. To monitor the vehicle 108, the front-end components 102 may include one or more sensors 120 installed within the vehicle 108 that may communicate with the on-board computer 114. The front-end components 102 may further process the sensor data using the on-board computer 114 or a mobile device 110 (e.g., a smart phone, a tablet computer, a special purpose computing device, etc.) to determine when the vehicle is in operation and information regarding the vehicle. In some embodiments of the system 100, the front-end components 102 may communicate with the back-end components 104 via a network 130. Either the on-board computer 114 or the mobile device 110 may communicate with the back-end components 104 via the network 130 to allow the back-end components 104 to record information regarding vehicle usage. The back-end components 104 may use one or more servers 140 to receive data from the front-end components 102, determine use and effectiveness of autonomous operation features, determine risk levels or premium price, and/or facilitate purchase or renewal of an autonomous vehicle insurance policy.

The front-end components 102 may be disposed within or communicatively connected to one or more on-board computers 114, which may be permanently or removably installed in the vehicle 108. The on-board computer 114 may interface with the one or more sensors 120 within the vehicle 108 (e.g., an ignition sensor, an odometer, a system clock, a speedometer, a tachometer, an accelerometer, a gyroscope, a compass, a geolocation unit, a camera, a distance sensor, etc.), which sensors may also be incorporated within or connected to the on-board computer 114. The front-end components 102 may further include a communication component 122 to transmit information to and receive information from external sources, including other vehicles, infrastructure, or the back-end components 104. In some embodiments, the mobile device 110 may supplement the functions performed by the on-board computer 114 described herein by, for example, sending or receiving information to and from the mobile server 140 via the network 130. In other embodiments, the on-board computer 114 may perform all of the functions of the mobile device 110 described herein, in which case no mobile device 110 may be present in the system 100. Either or both of the mobile device 110 or on-board computer 114 may communicate with the network 130 over links 112 and 118, respectively. Additionally, the mobile device 110 and on-board computer 114 may communicate with one another directly over link 116.

The mobile device 110 may be either a general-use personal computer, cellular phone, smart phone, tablet computer, phablet, wearable electronics, PDA (personal digital assistant), smart glasses, smart watches, smart bracelet, pager, computing device configured for wired or wireless RF (radio frequency) communication, a dedicated vehicle use monitoring device, and/or other mobile computing device. Although only one mobile device 110 is illustrated, it should be understood that a plurality of mobile devices 110 may be used in some embodiments. The on-board computer 114 may be a general-use on-board computer capable of performing many functions relating to vehicle operation or a dedicated computer for autonomous vehicle operation. Further, the on-board computer 114 may be installed by the manufacturer of the vehicle 108 or as an aftermarket modification or addition to the vehicle 108. In some embodiments or under certain conditions, the mobile device 110 or on-board computer 114 may function as thin-client devices that outsource some or most of the processing to the server 140.

The sensors 120 may be removably or fixedly installed within the vehicle 108 and may be disposed in various arrangements to provide information to the autonomous operation features. Among the sensors 120 may be included one or more of a GPS (Global Positioning System) unit, other satellite-based navigation unit, a radar unit, a LIDAR (Light Detection and Ranging) unit, an ultrasonic sensor, an infrared sensor, a camera, an accelerometer, a tachometer, and/or a speedometer. Some of the sensors 120 (e.g., radar, LIDAR, or camera units) may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.), lane markings, or signs or signals. Other sensors 120 (e.g., GPS, accelerometer, or tachometer units) may provide data for determining the location or movement of the vehicle 108. Other sensors 120 may be directed to the interior or passenger compartment of the vehicle 108, such as cameras, microphones, pressure sensors, thermometers, or similar sensors to monitor the vehicle operator and/or passengers within the vehicle 108. Information generated or received by the sensors 120 may be communicated to the on-board computer 114 or the mobile device 110 for use in autonomous vehicle operation.

In some embodiments, the communication component 122 may receive information from external sources, such as other vehicles or infrastructure. The communication component 122 may also send information regarding the vehicle 108 to external sources. To send and receive information, the communication component 122 may include a transmitter and a receiver designed to operate according to predetermined specifications, such as the dedicated short-range communication (DSRC) channel, wireless telephony, Wi-Fi, or other existing or later-developed communications protocols. The received information may supplement the data received from the sensors 120 to implement the autonomous operation features. For example, the communication component 122 may receive information that an autonomous vehicle ahead of the vehicle 108 is reducing speed, allowing the adjustments in the autonomous operation of the vehicle 108.

In further embodiments, the front-end components may include an infrastructure communication device 124 for monitoring the status of one or more infrastructure components 126. The infrastructure communication device 124 may include or be communicatively connected to one or more sensors (not shown) for detecting information relating to the condition of the infrastructure component 126. The sensors (not shown) may generate data relating to weather conditions, traffic conditions, or operating status of the infrastructure component 126. The infrastructure communication device 124 may be configured to receive the sensor data generated and determine a condition of the infrastructure component 126, such as weather conditions, road integrity, construction, traffic, available parking spaces, etc. The infrastructure communication device 124 may further be configured to communicate information to vehicles 108 via the communication component 122. In some embodiments, the infrastructure communication device 124 may receive information from the vehicles 108, while, in other embodiments, the infrastructure communication device 124 may only transmit information to the vehicles 108.

In addition to receiving information from the sensors 120, the on-board computer 114 may directly or indirectly control the operation of the vehicle 108 according to various autonomous operation features. The autonomous operation features may include software applications or routines implemented by the on-board computer 114 to control the steering, braking, or throttle of the vehicle 108. To facilitate such control, the on-board computer 114 may be communicatively connected to the controls or components of the vehicle 108 by various electrical or electromechanical control components (not shown). In embodiments involving fully autonomous vehicles, the vehicle 108 may be operable only through such control components (not shown). In other embodiments, the control components may be disposed within or supplement other vehicle operator control components (not shown), such as steering wheels, accelerator or brake pedals, or ignition switches.

In some embodiments, the front-end components 102 may communicate with the back-end components 104 via the network 130. The network 130 may be a proprietary network, a secure public internet, a virtual private network or some other type of network, such as dedicated access lines, plain ordinary telephone lines, satellite links, cellular data networks, combinations of these. Where the network 130 comprises the Internet, data communications may take place over the network 130 via an Internet communication protocol.

The back-end components 104 may include one or more servers 140. Each server 140 may include one or more computer processors adapted and configured to execute various software applications and components of the autonomous vehicle insurance system 100, in addition to other software applications. The server 140 may further include a database 146, which may be adapted to store data related to the operation of the vehicle 108 and its autonomous operation features. Such data might include, for example, dates and times of vehicle use, duration of vehicle use, use and settings of autonomous operation features, speed of the vehicle 108, RPM or other tachometer readings of the vehicle 108, lateral and longitudinal acceleration of the vehicle 108, incidents or near collisions of the vehicle 108, communication between the autonomous operation features and external sources, environmental conditions of vehicle operation (e.g., weather, traffic, road condition, etc.), errors or failures of autonomous operation features, or other data relating to use of the vehicle 108 and the autonomous operation features, which may be uploaded to the server 140 via the network 130. The server 140 may access data stored in the database 146 when executing various functions and tasks associated with the evaluating feature effectiveness or assessing risk relating to an autonomous vehicle.

Although the autonomous vehicle insurance system 100 is shown to include one vehicle 108, one mobile device 110, one on-board computer 114, and one server 140, it should be understood that different numbers of vehicles 108, mobile devices 110, on-board computers 114, and/or servers 140 may be utilized. For example, the system 100 may include a plurality of servers 140 and hundreds of mobile devices 110 or on-board computers 114, all of which may be interconnected via the network 130. Furthermore, the database storage or processing performed by the one or more servers 140 may be distributed among a plurality of servers 140 in an arrangement known as “cloud computing.” This configuration may provide various advantages, such as enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This may in turn support a thin-client embodiment of the mobile device 110 or on-board computer 114 discussed herein.

The server 140 may have a controller 155 that is operatively connected to the database 146 via a link 156. It should be noted that, while not shown, additional databases may be linked to the controller 155 in a known manner. For example, separate databases may be used for autonomous operation feature information, vehicle insurance policy information, and vehicle use information. The controller 155 may include a program memory 160, a processor 162 (which may be called a microcontroller or a microprocessor), a random-access memory (RAM) 164, and an input/output (O) circuit 166, all of which may be interconnected via an address/data bus 165. It should be appreciated that although only one microprocessor 162 is shown, the controller 155 may include multiple microprocessors 162. Similarly, the memory of the controller 155 may include multiple RAMs 164 and multiple program memories 160. Although the I/O circuit 166 is shown as a single block, it should be appreciated that the I/O circuit 166 may include a number of different types of I/O circuits. The RAM 164 and program memories 160 may be implemented as semiconductor memories, magnetically readable memories, or optically readable memories, for example. The controller 155 may also be operatively connected to the network 130 via a link 135.

The server 140 may further include a number of software applications stored in a program memory 160. The various software applications on the server 140 may include an autonomous operation information monitoring application 141 for receiving information regarding the vehicle 108 and its autonomous operation features, a feature evaluation application 142 for determining the effectiveness of autonomous operation features under various conditions, a compatibility evaluation application 143 for determining the effectiveness of combinations of autonomous operation features, a risk assessment application 144 for determining a risk category associated with an insurance policy covering an autonomous vehicle, and an autonomous vehicle insurance policy purchase application 145 for offering and facilitating purchase or renewal of an insurance policy covering an autonomous vehicle. The various software applications may be executed on the same computer processor or on different computer processors.

The various software applications may include various software routines stored in the program memory 160 to implement various modules using the process 162. Additionally, or alternatively, the software applications or routines may interact with various hardware modules that may be installed within or connected to the server 140. Such modules may implement part of all of the various exemplary methods discussed herein or other related embodiments. Such modules may include a vehicle control module for determining and implementing control decisions to operate the vehicle 108, a system status module for determining the operating status of autonomous operation features, a monitoring module for monitoring the operation of the vehicle 108, a remediation module for correcting abnormal operating states of autonomous operation features, an insurance module for determining risks and costs associated with operation of the vehicle 108, an alert module for generating and presenting alerts regarding the vehicle 108 or the vehicle operator, a risk assessment module for determining risks associated with operation of the vehicle 108 by the autonomous operation features or by the vehicle operator, an identification module for identifying or verifying the identity of the vehicle operator, an information module for obtaining information regarding a vehicle operator or vehicle 108, a use cost module for determining costs associated with operation of the vehicle 108, a comparison module for comparing one or more costs associated with owning or operating the vehicle 108, an update module for updating an autonomous operation feature of the vehicle 108, or other modules.

Exemplary Mobile Device or on-Board Computer

FIG. 2 illustrates a block diagram of an exemplary mobile device 110 and/or an exemplary on-board computer 114 consistent with the system 100. The mobile device 110 and/or on-board computer 114 may include a display 202, a GPS unit 206, a communication unit 220, an accelerometer 224, one or more additional sensors (not shown), a user-input device (not shown), and/or, like the server 140, a controller 204. In some embodiments, the mobile device 110 and on-board computer 114 may be integrated into a single device, or either may perform the functions of both. The on-board computer 114 (or mobile device 110) may interface with the sensors 120 to receive information regarding the vehicle 108 and its environment, which information may be used by the autonomous operation features to operate the vehicle 108.

Similar to the controller 155, the controller 204 may include a program memory 208, one or more microcontrollers or microprocessors (MP) 210, a RAM 212, and an I/O circuit 216, all of which are interconnected via an address/data bus 214. The program memory 208 may include an operating system 226, a data storage 228, a plurality of software applications 230, and/or a plurality of software routines 240. The operating system 226, for example, may include one of a plurality of general purpose or mobile platforms, such as the Android™, iOS®, or Windows® systems, developed by Google Inc., Apple Inc., and Microsoft Corporation, respectively. Alternatively, the operating system 226 may be a custom operating system designed for autonomous vehicle operation using the on-board computer 114. The data storage 228 may include data such as user profiles and preferences, application data for the plurality of applications 230, routine data for the plurality of routines 240, and other data related to the autonomous operation features. In some embodiments, the controller 204 may also include, or otherwise be communicatively connected to, other data storage mechanisms (e.g. one or more hard disk drives, optical storage drives, solid state storage devices, etc.) that reside within the vehicle 108.

As discussed with reference to the controller 155, it should be appreciated that although FIG. 2 depicts only one microprocessor 210, the controller 204 may include multiple microprocessors 210. Similarly, the memory of the controller 204 may include multiple RAMs 212 and multiple program memories 208. Although FIG. 2 depicts the I/O circuit 216 as a single block, the I/O circuit 216 may include a number of different types of I/O circuits. The controller 204 may implement the RAMs 212 and the program memories 208 as semiconductor memories, magnetically readable memories, or optically readable memories, for example.

The one or more processors 210 may be adapted and configured to execute any of one or more of the plurality of software applications 230 or any one or more of the plurality of software routines 240 residing in the program memory 204, in addition to other software applications. One of the plurality of applications 230 may be an autonomous vehicle operation application 232 that may be implemented as a series of machine-readable instructions for performing the various tasks associated with implementing one or more of the autonomous operation features according to the autonomous vehicle operation method 300. Another of the plurality of applications 230 may be an autonomous communication application 234 that may be implemented as a series of machine-readable instructions for transmitting and receiving autonomous operation information to or from external sources via the communication unit 220. Still another application of the plurality of applications 230 may include an autonomous operation monitoring application 236 that may be implemented as a series of machine-readable instructions for sending information regarding autonomous operation of the vehicle to the server 140 via the network 130.

The plurality of software applications 230 may call various of the plurality of software routines 240 to perform functions relating to autonomous vehicle operation, monitoring, or communication. In some embodiments, the plurality of software routines may further assess risk levels or determine insurance policy costs and adjustments. One of the plurality of software routines 240 may be a configuration routine 242 to receive settings from the vehicle operator to configure the operating parameters of an autonomous operation feature. Another of the plurality of software routines 240 may be a sensor control routine 244 to transmit instructions to a sensor 120 and receive data from the sensor 120. Still another of the plurality of software routines 240 may be an autonomous control routine 246 that performs a type of autonomous control, such as collision avoidance, lane centering, and/or speed control. In some embodiments, the autonomous vehicle operation application 232 may cause a plurality of autonomous control routines 246 to determine control actions required for autonomous vehicle operation. Similarly, one of the plurality of software routines 240 may be a monitoring and reporting routine 248 that monitors and transmits information regarding autonomous vehicle operation to the server 140 via the network 130. Yet another of the plurality of software routines 240 may be an autonomous communication routine 250 for receiving and transmitting information between the vehicle 108 and external sources to improve the effectiveness of the autonomous operation features.

Any of the plurality of software routines 240 may be designed to operate independently of the software applications 230 or in conjunction with the software applications 230 to implement modules associated with the methods discussed herein using the microprocessor 210 of the controller 204. Additionally, or alternatively, the software applications 230 or software routines 240 may interact with various hardware modules that may be installed within or connected to the mobile device 110 or the on-board computer 114. Such modules may implement part of all of the various exemplary methods discussed herein or other related embodiments.

For instance, such modules may include a vehicle control module for determining and implementing control decisions to operate the vehicle 108, a system status module for determining the operating status of autonomous operation features, a monitoring module for monitoring the operation of the vehicle 108, a remediation module for correcting abnormal operating states of autonomous operation features, an insurance module for determining risks and costs associated with operation of the vehicle 108, an alert module for generating and presenting alerts regarding the vehicle 108 or the vehicle operator, a risk assessment module for determining risks associated with operation of the vehicle 108 by the autonomous operation features or by the vehicle operator, an identification module for identifying or verifying the identity of the vehicle operator, an information module for obtaining information regarding a vehicle operator or vehicle 108, a use cost module for determining costs associated with operation of the vehicle 108, a comparison module for comparing one or more costs associated with owning or operating the vehicle 108, an update module for updating an autonomous operation feature of the vehicle 108, and/or other modules.

When implementing the exemplary autonomous vehicle operation method 300, the controller 204 of the on-board computer 114 may implement a vehicle control module by the autonomous vehicle operation application 232 to communicate with the sensors 120 to receive information regarding the vehicle 108 and its environment and process that information for autonomous operation of the vehicle 108. In some embodiments, including external source communication via the communication component 122 or the communication unit 220, the controller 204 may further implement a communication module based upon the autonomous communication application 234 to receive information for external sources, such as other autonomous vehicles, smart infrastructure (e.g., electronically communicating roadways, traffic signals, or parking structures), or other sources of relevant information (e.g., weather, traffic, local amenities). Some external sources of information may be connected to the controller 204 via the network 130, such as the server 140 or internet-connected third-party databases (not shown). Although the autonomous vehicle operation application 232 and the autonomous communication application 234 are shown as two separate applications, it should be understood that the functions of the autonomous operation features may be combined or separated into any number of the software applications 230 or the software routines 240.

In some embodiments, the controller 204 may further implement a monitoring module by the autonomous operation monitoring application 236 to communicate with the server 140 to provide information regarding autonomous vehicle operation. This may include information regarding settings or configurations of autonomous operation features, data from the sensors 120 regarding the vehicle environment, data from the sensors 120 regarding the response of the vehicle 108 to its environment, communications sent or received using the communication component 122 or the communication unit 220, operating status of the autonomous vehicle operation application 232 and the autonomous communication application 234, and/or commands sent from the on-board computer 114 to the control components (not shown) to operate the vehicle 108. The information may be received and stored by the server 140 implementing the autonomous operation information monitoring application 141, and the server 140 may then determine the effectiveness of autonomous operation under various conditions by implementing the feature evaluation application 142 and the compatibility evaluation application 143. The effectiveness of autonomous operation features and the extent of their use may be further used to determine risk associated with operation of the autonomous vehicle by the server 140 implementing a risk assessment module or insurance module associated with the risk assessment application 144.

In addition to connections to the sensors 120, the mobile device 110 or the on-board computer 114 may include additional sensors, such as the GPS unit 206 or the accelerometer 224, which may provide information regarding the vehicle 108 for autonomous operation and other purposes. Furthermore, the communication unit 220 may communicate with other autonomous vehicles, infrastructure, or other external sources of information to transmit and receive information relating to autonomous vehicle operation. The communication unit 220 may communicate with the external sources via the network 130 or via any suitable wireless communication protocol network, such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11 standards). WiMAX, Bluetooth, infrared or radio frequency communication, etc. Furthermore, the communication unit 220 may provide input signals to the controller 204 via the I/O circuit 216. The communication unit 220 may also transmit sensor data, device status information, control signals, and/or other output from the controller 204 to one or more external sensors within the vehicle 108, mobile devices 110, on-board computers 114, and/or servers 140.

The mobile device 110 and/or the on-board computer 114 may include a user-input device (not shown) for receiving instructions or information from the vehicle operator, such as settings relating to an autonomous operation feature. The user-input device (not shown) may include a “soft” keyboard that is displayed on the display 202, an external hardware keyboard communicating via a wired or a wireless connection (e.g., a Bluetooth keyboard), an external mouse, a microphone, or any other suitable user-input device. The user-input device (not shown) may also include a microphone capable of receiving user voice input.

Exemplary Autonomous Vehicle Operation Method

FIG. 3 illustrates a flow diagram of an exemplary autonomous vehicle operation method 300, which may be implemented by the autonomous vehicle insurance system 100. The method 300 may begin at block 302 when the controller 204 receives a start signal. The start signal may be a command from the vehicle operator through the user-input device to enable or engage one or more autonomous operation features of the vehicle 108. In some embodiments, the vehicle operator 108 may further specify settings or configuration details for the autonomous operation features. For fully autonomous vehicles, the settings may relate to one or more destinations, route preferences, fuel efficiency preferences, speed preferences, and/or other configurable settings relating to the operation of the vehicle 108. In some embodiments, fully autonomous vehicles may include additional features or settings permitting them to operate without passengers or vehicle operators within the vehicle. For example, a fully autonomous vehicle may receive an instruction to find a parking space within the general vicinity, which the vehicle may do without the vehicle operator. The vehicle may then be returned to a selected location by a request from the vehicle operator via a mobile device 110 or otherwise. This feature may further be adapted to return a fully autonomous vehicle if lost or stolen.

For other autonomous vehicles, the settings may include enabling or disabling particular autonomous operation features, specifying thresholds for autonomous operation, specifying warnings or other information to be presented to the vehicle operator, specifying autonomous communication types to send or receive, specifying conditions under which to enable or disable autonomous operation features, and/or specifying other constraints on feature operation. For example, a vehicle operator may set the maximum speed for an adaptive cruise control feature with automatic lane centering. In some embodiments, the settings may further include a specification of whether the vehicle 108 should be operating as a fully or partially autonomous vehicle. In embodiments where only one autonomous operation feature is enabled, the start signal may consist of a request to perform a particular task (e.g. autonomous parking) and/or to enable a particular feature (e.g., autonomous braking for collision avoidance). In other embodiments, the start signal may be generated automatically by the controller 204 based upon predetermined settings (e.g., when the vehicle 108 exceeds a certain speed and/or is operating in low-light conditions). In some embodiments, the controller 204 may generate a start signal when communication from an external source is received (e.g., when the vehicle 108 is on a smart highway or near another autonomous vehicle).

After receiving the start signal at block 302, the controller 204 may receive sensor data from the sensors 120 during vehicle operation at block 304. In some embodiments, the controller 204 may also receive information from external sources through the communication component 122 and/or the communication unit 220. The sensor data may be stored in the RAM 212 for use by the autonomous vehicle operation application 232. In some embodiments, the sensor data may be recorded in the data storage 228 and/or transmitted to the server 140 via the network 130. The sensor data may alternately either be received by the controller 204 as raw data measurements from one of the sensors 120 and/or may be preprocessed by the sensor 120 prior to being received by the controller 204. For example, a tachometer reading may be received as raw data and/or may be preprocessed to indicate vehicle movement or position. As another example, a sensor 120 comprising a radar and/or LIDAR unit may include a processor to preprocess the measured signals and send data representing detected objects in 3-dimensional space to the controller 204.

The autonomous vehicle operation application 232, other applications 230, and/or routines 240 may cause the controller 204 to process the received sensor data at block 306 in accordance with the autonomous operation features. The controller 204 may process the sensor data to determine whether an autonomous control action is required and/or to determine adjustments to the controls of the vehicle 108. For example, the controller 204 may receive sensor data indicating a decreasing distance to a nearby object in the vehicle's path and process the received sensor data to determine whether to begin braking (and, if so, how abruptly to slow the vehicle 108). As another example, the controller 204 may process the sensor data to determine whether the vehicle 108 is remaining with its intended path (e.g., within lanes on a roadway). If the vehicle 108 is beginning to drift or slide (e.g., as on ice or water), the controller 204 may determine appropriate adjustments to the controls of the vehicle to maintain the desired bearing. If the vehicle 108 is moving within the desired path, the controller 204 may nonetheless determine whether adjustments are required to continue following the desired route (e.g., following a winding road). Under some conditions, the controller 204 may determine to maintain the controls based upon the sensor data (e.g., when holding a steady speed on a straight road).

When the controller 204 determines an autonomous control action is required at block 308, the controller 204 may cause the control components of the vehicle 108 to adjust the operating controls of the vehicle to achieve desired operation at block 310. For example, the controller 204 may send a signal to open or close the throttle of the vehicle 108 to achieve a desired speed. Alternatively, the controller 204 may control the steering of the vehicle 108 to adjust the direction of movement. In some embodiments, the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 and/or the communication unit 220, which signal may be used by other autonomous vehicles to adjust their controls. As discussed further below, the controller 204 may also log or transmit the autonomous control actions to the server 140 via the network 130 for analysis.

The controller 204 may continue to receive and process sensor data at blocks 304 and 306 until an end signal is received by the controller 204 at block 312. The end signal may be automatically generated by the controller 204 upon the occurrence of certain criteria (e.g., the destination is reached or environmental conditions require manual operation of the vehicle 108 by the vehicle operator). Additionally, or alternatively, the vehicle operator may pause, terminate, and/or disable the autonomous operation feature or features using the user-input device or by manually operating the vehicle's controls, such as by depressing a pedal or turning a steering instrument. When the autonomous operation features are disabled or terminated, the controller 204 may either continue vehicle operation without the autonomous features or may shut off the vehicle 108, depending upon the circumstances.

Where control of the vehicle 108 must be returned to the vehicle operator, the controller 204 may alert the vehicle operator in advance of returning to manual operation. The alert may include a visual, audio, and/or other indication to obtain the attention of the vehicle operator. In some embodiments, the controller 204 may further determine whether the vehicle operator is capable of resuming manual operation before terminating autonomous operation. If the vehicle operator is determined not be capable of resuming operation, the controller 204 may cause the vehicle to stop and/or take other appropriate action.

Exemplary Monitoring Method During Operation

FIG. 4 illustrates a flow diagram depicting an exemplary monitoring method 400 during vehicle operation, which may be implemented by the autonomous vehicle insurance system 100. The method 400 may monitor the operation of the vehicle 108 and adjust risk levels and rates based upon vehicle use. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the method 400 may be implemented by the mobile device 110, the on-board computer 114, the server 140, or a combination thereof. Upon receiving an indication of vehicle operation at block 402, the on-board computer 114 may determine the configuration and operating status of the autonomous operation features (including the sensors 120 and the communication component 122) at block 404. The identity of the vehicle operator may be determined and/or verified at block 406, which identity may be used to determine or receive a vehicle operator profile at block 408. The vehicle operator profile may contain information regarding the vehicle operator's ability to manually operate the vehicle and/or past use of autonomous operation features by the vehicle operator. Information from the sensors 120 and/or external data from the communication component 122 may be used at block 410 to determine environmental conditions in which the vehicle 108 is operating. Together, this information determined at blocks 404-410 may be used at block 412 to determine one or more risk levels associated with operation of the vehicle, from which may be determined a costs associated with an insurance policy at block 414. In some embodiments, information regarding the determined cost may be presented to the vehicle operator or other insurance customer associated with the vehicle 108 at block 416. In still further embodiments, the vehicle operator and/or insurance customer may be presented with recommendations or options regarding the cost associated with the insurance policy at block 418. Presentation of options may assist the vehicle operator and/or insurance customer in reducing the cost by allowing the vehicle operator and/or insurance customer to select a lower-cost option (e.g. by adjusting the settings associated with the autonomous operation features). In some embodiments, the vehicle operator and/or insurance customer may be able to select one or more of the options to effect an adjustment in the risk levels and/or insurance cost.

The method 400 may continue monitoring operation of the vehicle 108 at block 420, and adjustments may be made to the risk levels and insurance costs. If the settings associated with the autonomous operation features are determined to have changed at block 422 (e.g., as a result of the vehicle operator taking manual operation of additional controls), the one or more risk levels may be determined based upon the new settings at block 412, in which case the blocks 414-422 may be repeated. When no changes have been made to the settings, the method 400 may further check for changes to the environmental conditions and/or operating status of the autonomous operation features at block 424. If changes are determined to have occurred at block 424, the one or more risk levels may be determined based upon the new settings at block 412, as at block 422. When no changes have occurred, the method 400 may determine whether vehicle operations are ongoing or whether operation is complete at block 426. When vehicle operation is ongoing, the method 400) may continue to monitor vehicle operation at block 420. When vehicle operation is complete, information regarding operation of the vehicle may be recorded at block 428, at which point the method 400 may terminate.

At block 402, the on-board computer 114 may receive an indication of vehicle operation. This indication may be received from the vehicle operator (either directly or through the mobile device 110), and/or it may be generated automatically. For example, the on-board computer 114 or the mobile device 110 may automatically generate an indication of vehicle operation when the vehicle starts operation (e.g., upon engine ignition, system power-up, movement of the vehicle 108, etc.). Upon receiving the indication of vehicle operation, the on-board computer 114 may initiate a system check and/or begin recording information regarding operation of the vehicle 108.

At block 404, the on-board computer 114 may determine the configuration and operating status of one or more autonomous operation features of the vehicle 108. This may include determining the configuration, settings, and/or operating status of one or more hardware or software modules for controlling part or all of the vehicle operation, aftermarket components disposed within the vehicle to provide information regarding vehicle operation, and/or sensors 120 disposed within the vehicle. In some embodiments, a software version, model version, and/or other identification of the feature or sensor may be determined. In further embodiments, the autonomous operation feature may be tested to assess proper functioning, which may be accomplished using a test routine or other means. Additionally, the sensors 120 or the communication component 122 may be assessed to determine their operating status (e.g., quality of communication connections, signal quality, noise, responsiveness, accuracy, etc.). In some embodiments, test signals may be sent to one or more of the sensors 120, responses to which may be received and/or assessed by the on-board computer to determine operating status. In further embodiments, signals received from a plurality of sensors may be compared to determine whether any of the sensors are malfunctioning. Additionally, signals received from the sensors may be used, in some embodiments, to calibrate the sensors.

At block 406, the on-board computer 114 may determine the identity of the vehicle operator. To determine the identity of the vehicle operator, the on-board computer 114 may receive and process information regarding the vehicle operator. In some embodiments, the received information may include sensor data from one or more sensors 120 configured to monitor the interior of the vehicle. For example, a camera or other photographic sensor may provide photographic information regarding the vehicle operator, which may be processed and compared with other photographic data for a plurality of persons to determine the identity of the vehicle operator. In further embodiments, the on-board computer may receive information from a mobile computing device associated with the vehicle operator, such as a mobile phone or wearable computing device. For example, a mobile phone may connect to the on-board computer 114, which may identify the vehicle operator. Additional steps may be taken to verify the identity of the vehicle operator, such as comparing a weight sensed on a seat or use of voice recognition algorithms.

At block 408, the on-board computer 114 may determine and/or access a vehicle operator profile based upon the identity of the vehicle operator determined at block 406. The vehicle operator profile may include information regarding the vehicle operator's style of operating the vehicle, including information regarding past operation of one or more vehicles by the vehicle operator. This information may further contain past vehicle operator selections of settings for one or more autonomous operation features. In some embodiments, the on-board computer 114 may request or access the vehicle operator profiled based upon the determined identity. In other embodiments, the on-board computer 114 may generate the vehicle operator profile from information associated with the identified vehicle operator. The vehicle operator profile may include information relating to one or more risks associated with operation of the vehicle by the vehicle operator. For example, the vehicle operator profile for a driver may include information relating to risk levels based upon past driving patters or habits in a variety of relevant environments, which may include risk levels associated with manual operation of the vehicle by the driver. In some embodiments, the vehicle operator profile may include information regarding default settings used by the vehicle operator for the autonomous operation features.

At block 410, the on-board computer 114 may determine environmental conditions within which the vehicle 108 is or is likely to be operating. Such environmental conditions may include weather, traffic, road conditions, time of day, location of operation, type of road, and/or other information relevant to operation of the vehicle. The environmental conditions may be determined based upon signals received from the sensors 120, from external data received through the communication component 122, and/or from a combination of sources. The environmental conditions may then be used in determining risk levels associated with operation of the vehicle 108.

At block 412, the on-board computer may determine one or more risk levels associated with operation of the vehicle 108. The risk levels may be determined based upon a combination of risk factors relating to the autonomous operation features and/or risk factors relating to the vehicle operation. Risks associated with the autonomous operation features may be determined based upon the configuration and/or operating status of the autonomous operation features, the settings of the autonomous operation features, and the vehicle environment. Risks associated with the vehicle operation may be determined based upon the autonomous operation features settings (i.e., the extent to which the vehicle operator will be controlling vehicle operations) and/or the vehicle operator profile. The combined risk may account for the likelihood of the autonomous operation features and/or the vehicle operator controlling vehicle operations with respect to relevant functions of the vehicle 108.

At block 414, the on-board computer 114 may determine a cost associated with an insurance policy based upon the one or more risks. In some embodiments, the server 140 may receive information regarding the vehicle operator and the autonomous operation features and/or may determine the cost associated with the insurance policy based upon the risks. The cost may be based upon risk levels associated with separate autonomous operation features, interaction between autonomous operation features, the design and capabilities of the vehicle 108, the past operating history of the vehicle operator as included in the vehicle operator profile, and/or other information regarding the probability of an accident, collision, and/or other loss event involving the vehicle 108. Each of the separate risks may depend upon the environmental conditions, and the risks may be weighted based upon the likelihood of each situation. In some embodiments, a total risk may be determined relating to operation of the vehicle under foreseeable conditions with specific settings and configurations of autonomous operation features by a specific vehicle operator. The total risk may be used to determine one or more costs associated with the insurance policy, such as a premium and/or discount.

In some embodiments, information regarding the cost associated with the insurance policy may be presented to the vehicle operator or insurance customer at block 416. The information may be presented by a display, such as the display 202 of the on-board computer 114 or the mobile device 110. The information may be presented either for informational purposes or to receive acceptance of the vehicle operator or insurance customer. The insurance cost information may include an indication of one or more of a premium, rate, rating, discount, reward, special offer, points level, program, refund, and/or other costs associated with one or more insurance policies. Additionally, or alternatively, summary information may be presented regarding insurance costs, including a risk level (e.g., high risk, low risk, a risk/cost level on a spectrum, etc.). In some embodiments, presentation of insurance cost information may be suppressed or delayed (e.g., cost information may be presented in summary form on a periodic billing statement).

In further embodiments, options or recommendations regarding the cost associated with the insurance policy may be presented to the vehicle operator or insurance customer at block 418. The options or recommendations may likewise be presented by a display, such as the display 202 of the on-board computer 114 and/or the mobile device 110. The options or recommendations may include information regarding costs associated with other settings or configurations of the autonomous operation features (e.g., enabling additional features, selecting an operating mode with lower risks under the determined environmental conditions, etc.). In some embodiments, the recommendations or options may be presented for informational purposes only, requiring the vehicle operator or insurance customer to make any adjustments separately (e.g., through a settings module or other means of adjusting settings for the autonomous operation features). In other embodiments, the vehicle operator or insurance customer may select one or more of the options, whereby adjustments to the configuration or settings of the autonomous operation features may be caused to be implemented by the on-board computer 114 or other controlling device. In some embodiments, the options or recommendations may include options or recommendations to update the software version of one or more autonomous operation features, in which case information regarding a cost associated with updating the features (if applicable) may be presented. Once the information and/or options or recommendations regarding insurance costs have been presented at blocks 416-418 (including, in some embodiments, while such presentation is occurring), the on-board computer 114 may monitor operation of the vehicle 108.

At block 420, the on-board computer 114 may monitor operation of the vehicle 108, including autonomous operation feature control decisions, signals from the sensors 120, external data from the communication component 122, and/or control decisions of the vehicle operator. Monitoring vehicle operation may include monitoring data received directly from the features, sensors, and/or other components, as well as summary information regarding the condition, movement, and/or environment of the vehicle 108. The on-board computer 114 and/or mobile device 110 may cause the operating data to be stored or recorded, either locally in the data storage 228 and/or via server 140 in the program memory 160 and/or the database 146. Monitoring may continue until vehicle operation is complete (e.g., the vehicle has reached its destination and shut down), including during any updates or adjustments.

At block 422, the on-board computer 114 may determine whether any changes have been made to the settings or configuration of the autonomous operation features. If such changes or adjustments have been made, the on-board computer 114 may proceed to determine new risk levels and insurance costs at blocks 412-414 and/or present the information to the vehicle operator or insurance customer at blocks 416-418, as discussed above. In some embodiments, minor changes below a minimum change threshold may be ignored when determining whether any changes have been made. In further embodiments, the cumulate effect of a plurality of such minor changes below the minimum change threshold may be considered as a change at block 422 when the cumulative effect of the minor changes reaches and/or exceeds the minimum change threshold. When no changes to the settings or configuration of the autonomous operation features are determined to have been made at block 422, the on-board computer 114 may further determine whether any changes in the environmental conditions and/or operating status of the autonomous operation features or sensors have occurred. Although these steps are illustrated separately for clarity, it should be understood that they may be further divided or combined in various embodiments.

At block 424, the on-board computer 114 may determine whether any changes have occurred to the environmental conditions of the vehicle 108 and/or the operating status of the autonomous operation features, sensors 120, or communication component 122. Such changes may occur when weather or traffic conditions change, when sensors 120 malfunction or become blocked by debris, and/or when the vehicle 108 leaves an area where external data is available via the communication component 122. When such changes occur, the risk levels associated with control of the vehicle 108 by the vehicle operator and the autonomous operation features may likewise change. Therefore, it may be advantageous to adjust the use of the autonomous operation features to account for such changes. Thus, the on-board computer 114 may proceed to determine new risk levels and insurance costs at blocks 412-414 and/or present the information to the vehicle operator or insurance customer at blocks 416-418, as discussed above, when such changes are determined to have occurred at block 424. Similar to the determination at block 422, minor changes below a minimum change threshold may be ignored at block 424, unless the cumulative effect of the changes reaches or exceeds the minimum change threshold. When no changes are determined to have occurred at block 424, the method 400 may continue to monitor the operation of the vehicle 108 until vehicle operation is determined to have ended.

At block 426, the on-board computer 114 may determine whether vehicle operations are complete. This may include determining whether a command to shut down the vehicle 108 has been received, whether the vehicle 108 has remained idle at a destination for a predetermined period of time, and/or whether the vehicle operator has exited the vehicle 108. Until operation is determined at block 426 to be complete (i.e., when the vehicle trip has concluded), the on-board computer 114 may continue to monitor vehicle operation at block 420, as discussed above. When operation is determined to be complete at block 426, the on-board computer 114 may further cause a record of the operation of the vehicle 108 to be made or stored. Such records may include operating data (in full or summary form) and may be used for assessing risks associated with one or more autonomous operation features or the vehicle operator. As noted above, in some embodiments, records of operating data may be generated and stored continually during operation. In some embodiments, the partial or completed records may be transmitted to the server 140 to be stored in the database 146. After completion and recordation of the vehicle operation, the method 400 may terminate.

Exemplary Methods for Determining Operating Status

FIG. 5 illustrates a flow diagram depicting an exemplary operating status assessment method 500 that may be used to determine operation status of the autonomous operation features, sensors 120, and/or communication component 122, as indicated in blocks 404 and 424 above. The method 500 may evaluate the autonomous operation features of the vehicle 108 (including sensors 120) and determine whether they are operating correctly, are malfunctioning, and/or are operating with impaired or degraded quality. Such determinations may be particularly important as the vehicle 108 ages or in environments that may block or damage sensors 120. In such cases, the original effectiveness of the autonomous operation features may be reduced as sensors become less accurate or processing of the sensor data is slowed (such as by software version updates that improve accuracy but require greater computational resources). The exemplary method 500 may be implemented regularly to ensure appropriate risk assessment, as well as periodically to certify the operating status level of the vehicle for roadworthiness or insurance rate adjustment. In some embodiments, periodic evaluation may be performed using special purpose computing devices and/or by licensed or authorized third parties. Periodic evaluation may further include more thorough testing and analysis of the vehicle 108, which may include testing at a test environment. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the method 500 may be implemented by the mobile device 110, the on-board computer 114, the server 140, or a combination thereof.

Upon receiving a request to determine the operating status of the autonomous operation features of the vehicle 108 at block 502, the configuration of the sensors 120 may be determined at block 504. The functioning of autonomous operation feature software routines may further be determined at block 506. A test signal may be transmitted to the sensors 120 at block 508, and/or sensor data may be received at block 510. The sensor data may include a response to the test signal, as well as other signals from the sensors based upon the vehicle environment or operation of the vehicle 108. Based upon the received information, the operating status of the autonomous operation features and components may be determined at block 512. If any impairments are detected at block 514, the method 500 may attempt to remediate the impairments at block 516. If impairments are detected to remain at block 518 after the remediation attempt, an alert may be generated and presented to the vehicle operator or an insurance customer at block 520. When no impairments are detected or after presentation of the alert, a report indicating the operational status of the autonomous operation features may be generated at block 522. In some embodiments, the report may be transmitted to an insurer at block 524, and/or a cost associated with an insurance policy associated with the vehicle 108 may be determined at block 526. The determined cost may be presented with the report at block 528 to the vehicle operator or insurance customer. Once the report has been presented, the exemplary method may end.

FIG. 6 illustrates a flow diagram of an exemplary operating status monitoring method 600 that may be used to determine operation status of the autonomous operation features, sensors 120, and/or communication component 122, in addition to or alternatively to the exemplary method 500 above. The method 600 may be implemented while the vehicle 108 is in operation to monitor the operating status of the autonomous operation features and components. The method 600 may monitor the vehicle operating data at block 602 to determine operating status of the autonomous operation features and components at block 604. When a change in operating status is detected at block 606, one or more corresponding changes in risk levels may be determined at block 608. If the changes in risk levels are determined to cause the risk levels to exceed an alert threshold but not a critical threshold at blocks 610 and 612, respectively, an alert is generated and presented to the vehicle operator at block 614. If the risk levels are determined to exceed the critical threshold at block 612, the method 600 may determine whether control of the vehicle 108 can be safely transferred to the vehicle operator at block 616. If the vehicle operator is prepared and able to assume control of the vehicle 108, then vehicle operation may be transferred to the vehicle operator at block 620. If control cannot be safely transferred, the vehicle 108 may cease operations and shut down at block 618. Once the vehicle 108 is no longer operating, the method 600 may terminate. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the method 600 may be implemented by the mobile device 110, the on-board computer 114, the server 140, or a combination thereof.

Exemplary Methods for Control Hand-Off

FIGS. 7A-B illustrate flow diagrams depicting exemplary vehicle operation hand-off methods 700 that may be used to transfer operation of the vehicle 108 from one or more autonomous operation features to the vehicle operator. FIG. 7A illustrates hand-off of control when determined necessary based upon heightened risk levels associated with operation by the one or more autonomous operation features under the environmental conditions. FIG. 7B illustrates hand-off of control when requested by the vehicle operator while one or more autonomous operation features are performing vehicle control tasks. The methods 700 illustrated in FIGS. 7A and 7B may be combined or separately implemented in various embodiments. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the exemplary method 700 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

Exemplary vehicle operation hand-off method 700 may be implemented at any time when one or more autonomous operation features are controlling part or all of the operation of the vehicle 108. The method 700 may begin by identifying the vehicle operator at block 702 and receiving a vehicle operator profile for the vehicle operator at block 704. At block 706, the operating data (including, in some embodiments, sensor data and external data) may be received and used to monitor operation of the vehicle 108. In some embodiments, a request to disable one or more autonomous operation features may be received from the vehicle operator at block 732. Autonomous risk levels associated with operation of the vehicle 108 by the autonomous operation features and operator risk levels associated with operation of the vehicle 108 by the vehicle operator may be determined at block 708 and 710, respectively. The determined risk levels may be used at block 712 to determine whether to disable one or more autonomous operation features. In some embodiments, the determination of whether to disable one or more autonomous operation features may further be based upon the preparedness level of the vehicle operator determined at block 716.

When it is determined to disable one or more autonomous operation features at block 714, the method 700 may transfer control to the vehicle operator. In some embodiments, this may include determining whether the vehicle operator is able to safely assume control by determining whether the vehicle operator's preparedness level is above a transfer threshold level at block 718. If so, an alert may be presented to the vehicle operator at block 720 to notify the vehicle operator of the transfer of control from the one or more autonomous operation features before transferring operation at block 722. If the vehicle operator's preparedness level is determined to be below the transfer threshold but above a minimum threshold at block 724, an alert may be presented at block 730 to attempt to prepare the vehicle operator to assume control if the risk levels associated with continued operation by the autonomous operation features do not exceed a critical risk threshold at block 726. Once the alert is presented to the vehicle operator at block 730, the vehicle operator's preparedness level may be determined again at block 716 and evaluated at block 718. If the risk levels exceed the critical threshold at block 726 or the vehicle operator's preparedness level is below the minimum threshold at block 724, the vehicle 108 may discontinue operation at block 728 and the method 700 may end.

When it is determined not to disable the one or more autonomous operation features at block 714, the method 700 may continue to monitor the operating data at block 706. If the vehicle operator requested that one or more autonomous operation features be disabled, the method 700 may present an alert at block 734 to notify the vehicle operator that disabling the autonomous operation features is not recommended. In some embodiments, options to override the determination not to disable the autonomous operation features may be presented to the vehicle operator at block 736, which the vehicle operator may select at block 738. If the vehicle operator is determined at block 740 to have not selected an option to override the determination, the method 700 may continue to monitor operation data at block 706. If the vehicle operator is determined at block 740 to have selected an option to override the determination, control of operation may be transferred from the one or more autonomous operation features to the vehicle operator. In some embodiments, one or more risk levels associated with disabling the autonomous operation features may be determined at block 742. If the risk levels are determined to be below a critical threshold at block 744, control may be transferred to the vehicle operator. If the risk levels meet or exceed the critical threshold at block 744, the vehicle 108 may instead discontinue operation at block 728 and the method 700 may end.

Exemplary Methods for Vehicle Operator Identification

FIG. 8 illustrates a flow diagram depicting an exemplary vehicle operator identification method 800 that may be used to adjust an insurance policy associated with the vehicle operator or vehicle 108. The exemplary method 800 may begin with receipt of a request to identify the vehicle operator of the vehicle at block 802. At blocks 804-810, the vehicle operator may be identified by sensor data or received indications of identity, such as from a mobile device 110. Once the identity of the vehicle operator has been determined (or cannot be determined), the method 800 may further determine whether the vehicle operator is authorized to operate the vehicle 108 at block 812. If the vehicle operator is not authorized, an alert may be generated and vehicle operations may be limited at blocks 814-816. If the vehicle operator is authorized, a vehicle operator profile associated with the vehicle operator may be obtained at block 818, and/or an insurance policy associated with the vehicle 108 or the vehicle operator may be identified at block 820. During vehicle operation, operating data of the vehicle 108 may be received and used to adjust the vehicle operator profile and the insurance policy at blocks 822-826. When vehicle operation has been determined to be complete at block 828, the method 800 may terminate. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the method 800 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

Exemplary Methods for Monitoring Use by Vehicle Operators

FIG. 9 illustrates a flow diagram depicting an exemplary vehicle operator use monitoring and evaluation method 900 that may be used to determine skill or risk levels associated with a vehicle operator or adjust an insurance policy. The exemplary method 900 may begin with determining the identity of the vehicle operator at block 902. If a vehicle operator profile can be found for the vehicle operator at block 904, the vehicle operator profile may be accessed at block 912. If no vehicle operator profile can be found for the vehicle operator at block 904, a vehicle operator profile may be created based upon vehicle operations and stored for future use at blocks 906-910. The newly created vehicle operator profile may be generated at block 908 with vehicle operating data from only a short time period or may include only information regarding configuration and/or settings of the autonomous operation features, in which case operation of the vehicle may continue after the vehicle operator profile is generated. If operation is determined to be ongoing at block 914, vehicle operation may be monitored and the vehicle operator profile may be updated at blocks 916-924. In some embodiments, the vehicle operator may be able to select an option of a mode for vehicle operation. If such a mode selection is detected at block 916, the settings of the autonomous operation features may be adjusted at block 918. Vehicle operation may be monitored at block 920 based upon received operating data, which may be used to determine adjustments to the vehicle operator profile at block 922. The adjustments may then be used to update the vehicle operator profile at block 924. When operation is complete, the method 900 may determine risk or skill levels associated with the vehicle operator at blocks 926-928. These determined levels may be used at blocks 930-934 to generate a report or adjust an insurance policy. Although the exemplary embodiment is described as primarily performed by the on-board computer 114, the method 900 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

The vehicle operator profile may include information regarding the vehicle operator, including an operating style of the vehicle operation. The operating style may include information regarding frequency with which the vehicle operator operates the vehicle manually, uses one or more autonomous operation features, selects one or more settings for the autonomous operation features, and/or takes control from the autonomous operation features under various conditions. The operating style may further include information regarding observed control decisions made by the vehicle operator, such as rate of acceleration, frequency of lane changes, use of vehicle signaling devices, distances maintained from other vehicles or pedestrians, and/or other aspects of vehicle operation. For example, vehicle operator decisions regarding how long to stop at a stop sign or when to begin accelerating from such a stop in the presence of other vehicles or pedestrians may be included in the operating style. The vehicle operator profile may further include information regarding vehicle operator skill levels, as described below. In some embodiments, the vehicle operator profile may include a risk profile or information regarding one or more risk levels associated with operation of the vehicle by the vehicle operator. Such risk levels may be associated with particular configurations or settings of autonomous operation features and/or particular conditions of the vehicle environment (e.g., time of day, traffic levels, weather, etc.).

In further embodiments, the vehicle operator profile may include information regarding attentiveness of the vehicle operator while the vehicle is being autonomously operated. For example, some vehicle operators may typically pay attention to road conditions while a vehicle is operating in a fully autonomous mode, while other vehicle operators may typically engage in other activities. In some embodiments, the vehicle operator profile may include information regarding decisions made by the vehicle operator regarding actions that would result in adjustments to costs associated with an insurance policy (e.g., accepting or rejecting recommendations to optimize autonomous operation feature use to lower insurance costs).

The vehicle operator profile or vehicle operator behavior data may indicate how well the specific vehicle operator drives in rain, snow, sleet, ice, heavy traffic, road construction, stop-and-go traffic, bumper-to-bumper traffic, country or rural traffic, and/or city or downtown street traffic. The current environment (or condition) may include or be rain, ice, snow, fog, heavy traffic, bumper-to-bumper traffic, road construction, city traffic, country or rural traffic, and/or may be associated with a type of road, such as a two-lane or four-lane highway, and/or downtown city street or other street having several traffic lights.

The operating mode may include one or more settings or configurations of autonomous operation features. For example, operating modes may include adjustments to settings that cause the autonomous operation features to control the vehicle 108 in a more or less aggressive manner with respect to speed, distance from other vehicles, distance from pedestrians, etc. As an example, the settings may cause the vehicle 108 to remain at least a minimum distance from other vehicles (which may depend upon vehicle speed or road conditions), and/or modes may set different minimum distances. Examples of modes may include a city driving mode, a highway driving mode, a rush operation mode, a smooth operation mode, a cautious mode, and/or a user-defined mode.

In some embodiments, an operating mode may be based upon the vehicle operator profile. For example, the vehicle profile may include information indicating an operating style of the vehicle operator based upon observations of control commands by the vehicle operator, which profile information may be used to generate an operation mode that mimics the style of the vehicle operator. Thus, if the vehicle operator typically stops at stop signs for a particular length of time, the operating style may mimic this length of time to provide an experience that seems normal or customary to the vehicle operator.

Exemplary Methods for Comparing Costs

FIG. 10 illustrates a flow diagram depicting an exemplary cost comparison method 1000 that may be used to compare costs associated with vehicles, some of which may include autonomous operation features. The exemplary method 1000 may begin by receiving a command to generate a comparison report between two or more alternative transportation options for an insurance customer or other user. The method 1000 may further receive information regarding one or more vehicle operators may be received at block 1004 and/or information regarding a first vehicle and a second vehicle at block 1006. The first and second vehicles may differ in autonomous operation features or other characteristics. Cost levels associated with obtaining, operating, and insuring the first vehicle and the second vehicle may be determined at block 1008, and/or a recommendation based upon the costs may be determined at block 1010. A report including the costs levels, recommendation, and/or related information may be generated at block 1012 and presented to the insurance customer or other user at block 1014. Additionally, one or more options may be presented along with the report at block 1016, such as options to perform another comparison or present additional information. If an option is selected at block 1018, the corresponding action may be performed at block 1020. Although the exemplary embodiment is described as primarily performed by the server 140, the method 1000 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

Exemplary Methods for Updating Autonomous Operation Features

FIG. 11 illustrates a flow diagram depicting an exemplary autonomous operation feature update method 1100 that may be used to identify, recommend, and/or install updates to autonomous operation features in appropriate autonomous or semi-autonomous vehicles. In some embodiments, the updates may include software version updates. The exemplary method 1100 may begin with the receipt of an indication of an available update to an autonomous operation feature at block 1102, which may include an update to a version of autonomous operation feature software. A plurality of vehicles having the autonomous operation feature may be identified at block 1104 based upon recorded features or communication with the plurality of vehicles. A change in one or more risk levels associated with the update may be determined for some or all of the identified vehicles at block 1106, and/or a change in a cost associated with one or more of the plurality of vehicles may be determined at block 1108. If the determined changes in risk levels or insurance costs meet certain criteria for installing the update at block 1110, a notification regarding the update may be presented to an insurance customer at block 1112. The notification may further include information regarding costs associated with the update. If an indication of acceptance of the update is received at block 1114, the update may be installed at block 1116. Although the exemplary embodiment is described as primarily performed by the server 140, the method 1100 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

At block 1102, the server 140 may receive an indication of an available update to an autonomous operation feature. The indication may include information identifying one or more particular autonomous operation features and/or versions of autonomous operation features for which an update is available. Such updates may include updates to more recent versions of autonomous operation feature software, as well as updates or upgrades to hardware such as sensors 120. Additionally, the indication may include one or more criteria associated with the update. In some embodiments, the indication may further include other relevant characteristics of autonomous or semi-autonomous vehicles that may impact the suitability of receiving the update. For example, the update may require sensors 120 not required by the original autonomous operation feature, and/or the update may require a minimum computing power of the on-board computer 114 and/or mobile device 110. Additionally, the update may only be available for certain classes of vehicles (e.g., light trucks or SUVs). In some embodiments, the indication of an available update may be received from a maker, installer, and/or reseller of autonomous or semi-autonomous vehicles. For example, an original equipment manufacturer of autonomous vehicles may request that the update be provided to some or all vehicles sold by the manufacturer with the autonomous operation feature. In this manner, the update may be similar to a vehicle recall. In further embodiments, the indication may include information specifying a set of vehicles that may receive the update without charge or at a reduced rate, while other vehicles may still be eligible to receive the update for a fee.

At block 1104, the server 140 may identify a plurality of autonomous or semi-autonomous vehicles having the autonomous operation feature. Vehicle identification may include searching vehicle records in the database 146 and/or requesting information regarding installed vehicle operation features from on-board computers 114 via the network 130. The plurality of vehicles may be identified based upon one or more requirements for the update (e.g., additional sensors, a sufficiently current version of the autonomous operation feature, etc.). In some embodiments, only vehicles associated with insurance policies by one or more insurers associated with the server 140 may be identified. In further embodiments, vehicle operators and/or insurance customers may select settings in advance to opt in or opt out of future autonomous operation features. Opting in may further allow software updates to be automatically downloaded and/or installed when available. Such opt-in or opt-out settings may further be limited by additional criteria, which must be met before a notification may be presented and/or the update may be automatically installed.

At block 1106, the server 140 may determine a change in one or more risk levels associated with the autonomous operation feature corresponding to the update for one or more vehicles identified at block 1104. For example, the update may cause a reduction in expected risk levels from operation of the autonomous operation feature. The determination of the risk level may depend upon the vehicle, the current version of the autonomous operation feature, other autonomous operation features, sensor configurations, settings typically used by one or more vehicle operators, vehicle operator skill levels or risk levels, location or condition of typical operation of the vehicle, and/or other factors. In some embodiments, determining the change in a risk level may include determining one or more risk levels associated with the updated autonomous operation feature. Determining risk levels for the updated autonomous operation feature may include virtual and/or physical testing of the updated autonomous operation feature. In some embodiments, virtual testing of the update may include testing based upon recorded operating data for vehicles including previous versions of the autonomous operation feature.

At block 1108, the server 140 may further determine a change in one or more costs associated with an insurance policy associated with each of one or more of the vehicles identified at block 1104. The costs may include insurance policy premiums, fees, discounts, and/or other charges associated with insurance covering the vehicle or use of the vehicle. The costs may be based upon the changes in risk levels determined at block 1106. The costs may further be based upon the risk levels associated with one or more vehicle operators of each vehicle.

At block 1110, the server 140 may determine whether one or more criteria associated with the update are met. The criteria may be related to the determined risk levels, cost levels, vehicle type, necessary components, current autonomous operation feature version, user settings, or other factors. In some embodiments, the criteria may include a threshold magnitude for the change in the one or more risk levels. For example, the criteria may require a reduction in one or more risk levels of at least the threshold magnitude (which may be greater than or equal to zero), such that only a sufficiently large negative change in risk levels may meet the criteria. Similarly, the criteria may require a reduction in one or more costs associated with the insurance policy of at least a threshold magnitude (which may again be greater than or equal to zero), such that the cost would need to be sufficiently lowered to meet the criteria. In further embodiments, the cost threshold may be associated with a cost of purchasing and/or installing the update. For example, the criteria may require a reduction in total cost over a certain time period (e.g., one year, eighteen months, three years, etc.) from installing the update to be at least as great as the cost of the update (e.g., the purchase price of the update). Criteria may be combined in some embodiments, such that multiple thresholds and/or requirements must be met. In this manner, updates may be filtered to determine which updates are worth implementing.

In some embodiments, however, some updates may be indicated to be operation and/or safety critical updates, which may be presented and/or installed regardless of other criteria. For example, an indication of a mandatory recall due to a high incidence of critical failures may be presented to vehicle operators or insurance customers for all identified vehicles, regardless of other criteria. Similarly, the criteria may include one or more pre-update risk levels associated with the autonomous operation feature as overriding criteria, such that the other criteria may not prevent notification and/or automatic installation if the pre-update risk levels are above one or more maximum safe thresholds. In further embodiments, the criteria may depend upon insurance coverage types and/or levels associated with the vehicle. For example, an update may be required to maintain certain types of coverage, in which case all vehicles associated with an insurance policy with the type of coverage may be deemed to meet the criteria at block 1110. When the criteria are not met at block 1110, the method 1100 may terminate. Thus, in some embodiments, the update may be presented and/or installed for only those vehicles of the plurality of vehicles identified at block 1104 for which the criteria are met. For example, only vehicles with changes in risk levels determined at block 1106 that meet the criteria may receive the update.

When the criteria are met for one or more identified vehicles, the server 140 may generate and/or cause to be presented to one or more insurance customers associated with each of the one or more vehicles meeting the criteria a notification regarding the update. The notification may be transmitted from the server 140 via the network 130 and/or presented to each insurance customer or other user via the display 202 and/or other means. In some embodiments, the notification regarding the update may include information regarding one or more changes in costs associated with an insurance policy. The changes in costs may be based upon information regarding the update, such as changes in risks, and/or may include the changes in costs determined at block 1108. In some embodiments, the notification may include additional information such as an indication of risk levels associated with the current autonomous operation feature and/or the updated autonomous operation feature, cost associated with the update, other financial or non-financial benefits associated with the update, compatibility of the update with other autonomous operation features or updates, and/or other relevant information regarding the update. In some embodiments, the notification may include one or more options regarding the update. Such options may include options to install the update, to schedule installation of the update, to decline the update, and/or to remind the insurance customer of the update at a later time. Where the update includes a plurality of optional features and/or versions, the options may further allow the insurance customer and/or other user to select from among the optional features and/or versions.

At block 1114, the server 140 may receive an indication of acceptance of the update from the insurance customer and/or other authorized user associated with a vehicle. If a plurality of options were presented with the notification, the indication of acceptance may include information regarding a user selection or an absence of a user selection (in which case default options may be used). In some embodiments, continued operation of the vehicle without indicating that the update is declined and/or without adjusting an insurance policy may be received as an indication of acceptance. Alternatively, such continued operation may be received as an indication of declination of the update. Of course, the indication may be received by the server 140 upon a selection by the insurance customer or other authorized user of an option to accept or decline the update.

At block 1116, the server 140 may cause the updated to be installed within the vehicle. This may include facilitating installation by placing an order for one or more components, scheduling an installation, and/or transmitting software associated with the update. In some embodiments, the server 140 may transmit software comprising the update to the on-board computer 114 and/or mobile device 110 for installation. In such embodiments, the installation may automatically occur when necessary conditions for the installation are met (e.g., when the vehicle is not in operation), or the installation may further require user verification or initiation before the software may be installed. In further embodiments, successful installation of the update may be verified by the mobile-device 110, the on-board computer 114, and/or the server 140. The update may also be recorded in the database 146. Where a cost associated with an insurance policy changes based upon the update, the server 140 may further cause the insurance policy to be adjusted to account for the update. Such adjustment may be implemented by a change in risk levels and/or a change in direct cost associated with the insurance policy.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. In certain aspects, with the customer's permission, a computer-implemented method for updating an autonomous operation feature may be provided. An indication of a software update associated with the autonomous operation feature may be received, and several autonomous or semi-autonomous vehicles having the feature may be identified. The update may be installed within the several vehicles, such as via wireless communication. Also, a change in a risk level associated with the update to the autonomous operation feature may be determined, and an insurance discount may be determined or adjusted. As a result, an insurance discount may be provided to risk averse customers that affirmatively share their vehicle data with an insurance provider, and promptly and remotely receive new versions of software that operate autonomous vehicle safety features.

With the foregoing, a customer may opt into a rewards or other type of program, and willingly share their vehicle data with an insurance provider. In return, risk averse drivers and vehicle owners may receive discounts or insurance cost savings related to auto and other types of insurance from the insurance provider.

Software Versions

In one aspect, a computer-implemented method of updating an insurance policy may be provided. The method may include (1) gathering or receiving, at or via one or more processors (such as either a local processor associated with a smart vehicle and/or a remote processor or server associated with an insurance provider), data indicative of a software version installed on or in an insured vehicle that is associated with an autonomous or semi-autonomous functionality; (2) determining, at or via the one or more processors, that the software version is out of date or a (safer or less risky) new software version exists and/or is available for download; (3) generating, at or via the one or more processors, a recommendation to an insured to update or upgrade to the new software version, and transmitting that recommendation under the direction or control of the one or more processors to a mobile device or insured vehicle controller (such as via wireless communication or data transmission); (4) determining, at or via the one or more processors, (or receiving an indication) that the insured has updated or upgraded to the new software version associated with the autonomous or semi-autonomous functionality; and/or (5) updating or adjusting, at or via the one or more processors, an insurance policy (such as a premium, rate, rewards or points program, discount, etc.) for the insured vehicle based upon the insured updating or upgrading to the new software version associated with an autonomous or semi-autonomous functionality to facilitate providing cost savings to the insured and/or enticing drivers to update vehicle software to most recent versions and/or versions that perform better. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.

Exemplary Methods for Repair of an Autonomous Vehicle

FIG. 12 illustrates a flow diagram depicting an exemplary autonomous vehicle repair method 1200 that may be used to determine repairs needed as a result of damage to an autonomous or semi-autonomous vehicle. The exemplary method 1200 may begin by receiving an indication of damage to the vehicle 108 at block 1202 and/or receiving operating data associated with the vehicle 108 at block 1204. Based upon the operating data, the type and extent of damage to the vehicle 108 may be determined at block 1206, and/or repairs needed to fix the damage may be determined at block 1208. Additionally, one or more expected costs (or ranges of costs) for the repairs may be estimated at block 1210. An insurance policy associated with the vehicle 108 may be identified, and/or a maximum payment for the repairs may be determined at block 1212 based upon the estimated costs and the insurance policy. Information regarding the estimated cost or costs and the maximum payment under the insurance policy may be presented to an insurance customer at block 1214. Additionally, options associated with the repairs may be presented to the insurance customer at block 1216, and/or a selection of one or more options may be received at block 1218. An insurer or other party may cause a payment to be made at block 1220 to the insurance customer, beneficiary, or other relevant party based upon the estimated costs of repairing the damage and the selected option. Although the exemplary embodiment is described as primarily performed by the server 140, the method 1200 may be implemented by the mobile device 110, the on-board computer 114, the server 140, and/or a combination thereof.

Exemplary Methods for Infrastructure Communications

FIG. 13 illustrates a flow diagram depicting an exemplary infrastructure communication method 1300 that may be used to detect and communicate information regarding infrastructure components to vehicles. The exemplary method 1300 may begin with the infrastructure communication device 124 receiving information regarding the infrastructure component 126 from one or more sensors at block 1302. The information may be used at block 1304 to determine a message regarding the infrastructure component 126. In some embodiments, the message may be augmented at block 1306 by information associated with a sponsor or other party affiliated with the infrastructure communication device 124. The message may then be encoded at block 1308 and transmitted at block 1310, which may cause the message to be presented to the vehicle operator of the vehicle 108 at block 1312. Although the exemplary embodiment describes one infrastructure communication device 124 communicating with one vehicle 108, it should be understood than any number of infrastructure communication devices 124 may communicate with any number of vehicles 108.

Updating Insurance Policies

In one aspect, a computer-implemented method of updating an insurance policy may be provided. The method may include (a) gathering or receiving, at or via one or more processors (such as either a local processor associated with a smart vehicle and/or a remote processor or server associated with an insurance provider), data indicative of (1) vehicle usage, and/or (2) vehicle drivers for an insured vehicle; (b) analyzing the data, via the one or more processors, to determine (i) an amount and/or (ii) type of vehicle usage for each vehicle driver; (c) based upon the amount of vehicle usage for each vehicle driver and/or the type of vehicle usage for each vehicle driver, via the one or more processors, updating or adjusting an insurance policy (such as a premium, rate, rewards or points program, discount, etc.) for the insured vehicle; (d) transmitting, under the direction or control of the one or more processors, the updated or adjusted insurance policy (or otherwise causing the updated or adjusted insurance policy to be presented or displayed to the insured) to a mobile device of the insured for their review, modification, and/or approval; and/or (e) receiving, at or via the one or more processors, from the mobile device of the insured (such as via wireless communication) an approval of the updated or adjusted insurance policy of the insured to facilitate more accurate insurance pricing and/or insurance cost savings. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.

For instance, the amount of vehicle usage may include an amount of time and/or miles that each individual vehicle driver drives the vehicle. The type of vehicle usage may include characterizing various periods of driving and/or trips as city driving; country driving; freeway or highway driving city street driving; heavy traffic or congested traffic driving; driving in good weather; driving in hazardous weather; rush hour driving; and/or time-of-day driving.

The vehicle drivers may be identified from mobile device signature; seat pressure sensors and weight; image recognition techniques performed upon images of the driver; and/or biometric devices (such as heart beat or rate characteristics; voice print; and/or thumb or finger prints).

Biometric Device Data

In one aspect, a computer-implemented method of updating an insurance policy using biometric device data may be provided. The method may include (a) gathering or receiving, at or via one or more processors (such as either a local processor associated with a smart vehicle and/or a remote processor or server associated with an insurance provider), data from a biometric device indicative of whom is driving an insured vehicle; (b) gathering or receiving, at or via the one or more processors, data indicative of vehicle usage for a single trip and/or driving or driver behavior during the single trip; (c) updating or adjusting, at or via the one or more processors, an insurance policy (such as a premium, rate, rewards or points program, discount, etc.) for the insured vehicle based upon (1) whom is driving the insured vehicle (and/or his or her driving profile or score), and/or (2) the data indicative of vehicle usage for the single trip and/or the driving or driver behavior exhibited during the single trip to facilitate more accurate risk assessment and/or cost savings to the insured. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein. For instance, the biometric device may verify an identity of the driver based upon heartbeat, facial recognition techniques, and/or mood.

In another aspect, a computer-implemented method of updating an insurance policy may be provided. The method may include (1) gathering or receiving, at or via one or more processors (such as either a local processor associated with a smart vehicle and/or a remote processor or server associated with an insurance provider), data from a biometric device identifying a driver of an insured vehicle; (2) gathering or receiving, at or via the one or more processors, data indicative of driving or driver behavior for the driver identified from the biometric device data; (3) generating, at or via the one or more processors, a usage-based insurance policy for the insured vehicle based upon (i) the identity of the driver determined from the biometric device data, and/or (ii) the data indicative of driving or driver behavior exhibited by the driver to facilitate more accurate risk assessment and/or provide cost savings to the insured. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.

Usage-Based Insurance for Multiple Drivers

In one aspect, a computer-implemented method of generating or updating an usage-based insurance policy for an autonomous or semi-autonomous vehicle having multiple drivers may be provided. The method may include (1) identifying, via one or more processors (such as a vehicle-mounted local processor or remote insurance provider processor or server), multiple drivers that drive a vehicle, the vehicle having one or more autonomous or semi-autonomous technologies or functionalities; (2) determining, via the one or more processors, a driving behavior for each of the multiple drivers; (3) determining, via the one or more processors, (i) an amount that each autonomous or semi-autonomous technology or functionality is used, and/or (ii) a type of autonomous or semi-autonomous technology or functionality that is employed by each of the multiple drivers; (4) generating or updating, via the one or more processors, an insurance policy (such as a premium, rate, discount, rewards or points program, etc.) for the vehicle based upon (a) the driving behavior for each of the multiple drivers determined, (b) the amount that each autonomous or semi-autonomous technology or functionality is used by each of the multiple drivers, and/or (c) the type of autonomous or semi-autonomous technology or functionality that is employed by each of the multiple drivers to facilitate providing insurance-related cost savings to the insured and/or to provide an enticement to an insured family to implement safety enhancing technology or functionality. The method may include additional, fewer, or alternate actions, including those discussed elsewhere herein.

Exemplary Autonomous Vehicle Insurance Risk and Price Determination Methods

Risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles. In some embodiments or under some conditions, the vehicle 108 may be a fully autonomous vehicle operating without a vehicle operator's input or presence. In other embodiments or under other conditions, the vehicle operator may control the vehicle 108 with or without the assistance of the vehicle's autonomous operation features. For example, the vehicle may be fully autonomous only above a minimum speed threshold or may require the vehicle operator to control the vehicle during periods of heavy precipitation. Alternatively, the autonomous vehicle may perform all relevant control functions using the autonomous operation features under all ordinary operating conditions. In still further embodiments, the vehicle 108 may operate in either a fully or a partially autonomous state, while receiving or transmitting autonomous communications.

Where the vehicle 108 operates only under fully autonomous control by the autonomous operation features under ordinary operating conditions or where control by a vehicle operator may be disregarded for insurance risk and price determination, the risk level or premium associated with an insurance policy covering the autonomous vehicle may be determined based upon the risks associated with the autonomous operation features, without reference to risks associated with the vehicle operator. Where the vehicle 108 may be operated manually under some conditions, the risk level or premium associated with an insurance policy covering the autonomous vehicle may be based upon risks associated with both the autonomous operation features and the vehicle operator performing manual vehicle operation. Where the vehicle 108 may be operated with the assistance of autonomous communications features, the risk level or premium associated with an insurance policy covering the autonomous vehicle may be determined based in part upon a determination of the expected use of autonomous communication features by external sources in the relevant environment of the vehicle 108 during operation of the vehicle 108.

Data Acquisition

In one aspect, the present embodiments may relate to data acquisition. Data may be gathered via devices employing wireless communication technology, such as Bluetooth or other IEEE communication standards. In one embodiment, a Bluetooth enabled smartphone or mobile device, and/or an in-dash smart and/or communications device may collect data. The data associated with the vehicle, and/or vehicle or driver performance, that is gathered or collected at, or on, the vehicle may be wirelessly transmitted to a remote processor or server, such as a remote processor or server associated with an insurance provider. The mobile device 110 may receive the data from the on-board computer 114 or the sensors 120, and may transmit the received data to the server 140 via the network 130, and the data may be stored in the database 146. In some embodiments, the transmitted data may include real-time sensor data, a summary of the sensor data processed sensor data, operating data, environmental data, communication data, or a log such data.

Data may be generated by autonomous or semi-autonomous vehicles and/or vehicle mounted sensors (or smart sensors), and then collected by vehicle mounted equipment or processors, including Bluetooth devices, and/or an insurance provider remote processor or server. The data gathered may be used to analyze vehicle decision making. A processor may be configured to generate data on what an autonomous or semi-autonomous vehicle would have done in a given situation had the driver not taken over manual control/driving of the vehicle or alternative control actions not taken by the autonomous or semi-autonomous operation features. This type of control decision data (related to vehicle decision making) may be useful with respect to analyzing hypothetical situations.

In one embodiment, an application, or other computer or processor instructions, may interact with a vehicle to receive and/or retrieve data from autonomous or semi-autonomous processors and sensors. The data retrieved may be related to radar, cameras, sensor output, computer instructions or application output. Other data related to a smart vehicle controller, car navigation unit information (including route history information and typical routes taken), GPS unit information, odometer and/or speedometer information, and smart equipment data may also be gathered or collected. The application and/or other computer instructions may be associated with an insurance provider remote processor or server.

The control decision data may further include information regarding control decisions generated by one or more autonomous operation features within the vehicle. The operating data and control decision data gathered, collected, and/or acquired may facilitate remote evaluation and/or analysis of what the autonomous or semi-autonomous vehicle was “trying to do” (brake, slow, turn, accelerate, etc.) during operation, as well as what the vehicle actually did do. The data may reveal decisions, and the appropriateness thereof, made by the artificial intelligence or computer instructions associated with one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment. The data may include information related to what the vehicle would have done in a situation if the driver had not taken over (beginning manual vehicle control). Such data may include both the control actions taken by the vehicle and control actions the autonomous or semi-autonomous operation features would have caused the vehicle to take. Thus, in some embodiments, the control decisions data may include information regarding control decisions not implemented by the autonomous operation features to control the vehicle. This may occur when an autonomous operation feature generates a control decision or associated control signal, but the control decision or signal is prevented from controlling the vehicle because the autonomous feature or function is disabled, the control decision is overridden by the vehicle operator, the control signal would conflict with another control signal generated by another autonomous operation feature, a more preferred control decision is generated, and/or an error occurs in the on-board computer 114 or the control system of the vehicle.

For example, a vehicle operator may disable or constrain the operation of some or all autonomous operation features, such as where the vehicle is operated manually or semi-autonomously. The disabled or constrained autonomous operation features may, however, continue to receive sensor data and generate control decision data that is not implemented. Similarly, one or more autonomous operation features may generate more than one control decision in a relevant period of time as alternative control decisions. Some of these alternative control decisions may not be selected by the autonomous operation feature or an autonomous operation control system to control the vehicle. For example, such alternative control decisions may be generated based upon different sets of sensor or communication data from different sensors 120 or include or excluding autonomous communication data. As another example, the alternative control decisions may be generated faster than they can be implemented by the control system of the vehicle, thus preventing all control decisions from being implemented.

In addition to control decision data, other information regarding the vehicle, the vehicle environment, or vehicle operation may be collected, generated, transmitted, received, requested, stored, and/or recorded in connection with the control decision data. Additional operating data including sensor data from the sensors 120, autonomous communication data from the communication component 122 or the communication unit 220, location data, environmental data, time data, settings data, configuration data, and/or other relevant data may be associated with the control decision data. In some embodiments, a database or log may store the control decision data and associated information. In further embodiments, the entries in such log or database may include a timestamp indicating the date, time, location, vehicle environment, vehicle condition, autonomous operation feature settings, and/or autonomous operation feature configuration information associated with each entry. Such data may facilitate evaluating the autonomous or semi-autonomous technology, functionality, system, and/or equipment in hypothetical situations and/or may be used to calculate risk, and in turn adjust insurance policies, premiums, discounts, etc.

Autonomous Vehicle Insurance Policies

The disclosure herein relates to insurance policies for vehicles with autonomous operation features. Accordingly, as used herein, the term “vehicle” may refer to any of a number of motorized transportation devices. A vehicle may be a car, truck, bus, train, boat, plane, motorcycle, snowmobile, other personal transport devices, etc. Also as used herein, an “autonomous operation feature” of a vehicle means a hardware or software component or system operating within the vehicle to control an aspect of vehicle operation without direct input from a vehicle operator once the autonomous operation feature is enabled or engaged. The term “autonomous vehicle” means a vehicle including at least one autonomous operation feature. A “fully autonomous vehicle” means a vehicle with one or more autonomous operation features capable of operating the vehicle in the absence of or without operating input from a vehicle operator.

Additionally, the term “insurance policy” or “vehicle insurance policy,” as used herein, generally refers to a contract between an insurer and an insured. In exchange for payments from the insured, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid by or on behalf of the insured upon purchase of the insurance policy or over time at periodic intervals. Although insurance policy premiums are typically associated with an insurance policy covering a specified period of time, they may likewise be associated with other measures of a duration of an insurance policy, such as a specified distance traveled or a specified number of trips. The amount of the damages payment is generally referred to as a “coverage amount” or a “face amount” of the insurance policy. An insurance policy may remain (or have a status or state of) “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy. An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when the parameters of the insurance policy have expired, when premium payments are not being paid, when a cash value of a policy falls below an amount specified in the policy, or if the insured or the insurer cancels the policy.

The terms “insurer,” “insuring party,” and “insurance provider” are used interchangeably herein to generally refer to a party or entity (e.g., a business or other organizational entity) that provides insurance products, e.g., by offering and issuing insurance policies. Typically, but not necessarily, an insurance provider may be an insurance company. The terms “insured,” “insured party,” “policyholder,” and “customer” are used interchangeably herein to refer to a person, party, or entity (e.g., a business or other organizational entity) that is covered by the insurance policy, e.g., whose insured article or entity is covered by the policy. Typically, a person or customer (or an agent of the person or customer) of an insurance provider fills out an application for an insurance policy. In some cases, the data for an application may be automatically determined or already associated with a potential customer. The application may undergo underwriting to assess the eligibility of the party and/or desired insured article or entity to be covered by the insurance policy, and, in some cases, to determine any specific terms or conditions that are to be associated with the insurance policy, e.g., amount of the premium, riders or exclusions, waivers, and the like. Upon approval by underwriting, acceptance of the applicant to the terms or conditions, and payment of the initial premium, the insurance policy may be in-force. (i.e., the policyholder is enrolled).

Although the exemplary embodiments discussed herein relate to automobile insurance policies, it should be appreciated that an insurance provider may offer or provide one or more different types of insurance policies. Other types of insurance policies may include, for example, commercial automobile insurance, inland marine and mobile property insurance, ocean marine insurance, boat insurance, motorcycle insurance, farm vehicle insurance, aircraft or aviation insurance, and other types of insurance products.

Other Matters

Although the text herein sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One could implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.

In one aspect, autonomous or semi-autonomous vehicle; telematics; interconnected home; mobile device; and/or other data, including that discussed elsewhere herein, may be collected or received by an insurance provider remote server, such as via direct or indirect wireless communication or data transmission, after a customer affirmatively consents or otherwise opts into an insurance discount, reward, or other program. The insurance provider may then analyze the data received with the customer's permission to provide benefits to the customer. As a result, risk averse customers may receive insurance discounts or other insurance cost savings based upon data that reflects low risk behavior and/or technology that mitigates or prevents risk to (i) insured assets, such as autonomous or semi-autonomous vehicles, and/or (ii) autonomous or semi-autonomous vehicle operators or passengers.

It should also be understood that, unless a term is expressly defined in this patent using the sentence. “As used herein, the term ‘_’ is hereby defined to mean . . . ” or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based upon any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this disclosure is referred to in this disclosure in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based upon the application of 35 U.S.C. § 112(f).

Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (code embodied on a non-transitory, tangible machine-readable medium) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g. as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g. as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware is temporarily configured (e.g., programmed), the hardware need not be configured or instantiated at any one instance in time. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. Hardware elements can provide information to, and receive information from, other hardware elements. Accordingly, the described hardware may be regarded as being communicatively coupled.

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. Similarly, the methods or routines described herein may be at least partially processor-implemented. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information. As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. For example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled.” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description. In this description, and the claims that follow, the singular also includes the plural unless it is obvious that it is meant otherwise. This detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One could implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this application.

Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for system and a method for assigning mobile device data to a vehicle through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.

The particular features, structures, or characteristics of any specific embodiment may be combined in any suitable manner and in any suitable combination with one or more other embodiments, including the use of selected features without corresponding use of other features. In addition, many modifications may be made to adapt a particular application, situation or material to the essential scope and spirit of the present invention. It is to be understood that other variations and modifications of the embodiments of the present invention described and illustrated herein are possible in light of the teachings herein and are to be considered part of the spirit and scope of the present invention.

Claims

1. A computer-implemented method for updating an autonomous operation feature, comprising: receiving, at one or more processors, an indication of an update associated with the autonomous operation feature for an autonomous or semi-autonomous vehicle having the autonomous operation feature;collecting, by one or more processors, operating data for the autonomous or semi-autonomous vehicle during operation of the autonomous or semi-autonomous vehicle by electronic communication with an on-board computer of the vehicle, wherein the operating data includes data regarding operation of the autonomous operation feature and environmental conditions during operation of the autonomous or semi-autonomous vehicle;generating, by one or more processors, at least one risk level associated with the update to the autonomous operation feature based upon virtual or physical testing of the update associated with the autonomous operation feature; anddetermining, by one or more processors, a weighted risk level for the autonomous or semi-autonomous vehicle, wherein the weighted risk level is determined based upon the at least one risk level and the operating data to facilitate installation of updates to autonomous operation features.
2. The computer-implemented method of claim 1, the method further comprising: determining, by one or more processors, whether a risk level change associated with installation of the update for the autonomous or semi-autonomous vehicle meets one or more risk-related criteria for installing the update; andcausing, by one or more processors, the update to be installed within the autonomous or semi-autonomous vehicle when the risk level change meets the one or more risk-related criteria.
3. The computer-implemented method of claim 2, wherein the one or more risk-related criteria include a reduction in risk of at least a threshold magnitude, which threshold magnitude is greater than or equal to zero.
4. The computer-implemented method of claim 2, further including: determining, by one or more processors, a change in at least one cost associated with an insurance policy based upon the determined risk level change associated with the autonomous or semi-autonomous vehicle,wherein the one or more risk-related criteria include a reduction in the at least one cost associated with the insurance policy of at least a threshold magnitude, which threshold magnitude is greater than or equal to zero.
5. The computer-implemented method of claim 2, further including: causing, by one or more processors, a notification regarding the update to be presented to an insurance customer associated with the autonomous or semi-autonomous vehicle; andreceiving, at one or more processors, an indication of acceptance of the update from the insurance customer;wherein causing the update to be installed within the autonomous or semi-autonomous vehicle includes transmitting, by an update module, the update to the autonomous or semi-autonomous vehicle in response to the received indication of acceptance.
6. The computer-implemented method of claim 5, wherein the notification regarding the update includes information regarding a cost associated with the update and information regarding a change in at least one cost associated with an insurance policy associated with the autonomous or semi-autonomous vehicle.
7. The computer-implemented method of claim 2, wherein the indication of the update associated with the autonomous operation feature includes a request to provide the update to the autonomous or semi-autonomous vehicle.
8. The computer-implemented method of claim 2, wherein the update comprises an update to a version of software associated with the autonomous operation feature.
9. The computer-implemented method of claim 2, further including: determining, by one or more processors, that the update has been installed within the autonomous or semi-autonomous vehicle; andadjusting, by one or more processors, an insurance policy associated with the autonomous or semi-autonomous vehicle once the update is determined to have been installed.
10. A computer system for updating an autonomous operation feature, comprising: one or more processors; anda program memory coupled to the one or more processors and storing executable instructions that when executed by the one or more processors cause the computer system to: receive an indication of an update associated with the autonomous operation feature for an autonomous or semi-autonomous vehicle having the autonomous operation feature;collect operating data for the autonomous or semi-autonomous vehicle during the operation of the autonomous or semi-autonomous vehicle by electronic communication with an on-board computer of the vehicle, wherein the operating data includes data regarding operation of the autonomous operation feature and environmental conditions during operation of the autonomous or semi-autonomous vehicle;generate at least one risk level associated with the update to the autonomous operation feature based upon virtual or physical testing of the update associated with the autonomous operation feature; anddetermine a weighted risk level for the autonomous or semi-autonomous vehicle, wherein the weighted risk level is determined based upon the at least one risk level and the operating data to facilitate installation of updates to autonomous operation features.
11. The computer system of claim 10, wherein the executable instructions, when executed by the one or more processors, further cause the computer system to: determine a risk level change associated with installation of the update for the autonomous or semi-autonomous vehicle;determine whether the risk level change meets one or more risk-related criteria for installing the update; andcause the update to be installed within the autonomous or semi-autonomous vehicle when the risk level change meets the one or more risk-related criteria.
12. The computer system of claim 11, wherein the program memory further includes executable instructions that cause the computer system to: determine a change in at least one cost associated with an insurance policy based upon the determined risk level change associated with the autonomous or semi-autonomous vehicle,wherein the one or more risk-related criteria include a reduction in the at least one cost associated with the insurance policy of at least a threshold magnitude, which threshold magnitude is greater than or equal to zero.
13. The computer system of claim 10, wherein the indication of the update associated with the autonomous operation feature includes a request to provide the update to the autonomous or semi-autonomous vehicle having the autonomous operation feature.
14. The computer system of claim 10, wherein the update comprises an update to a version of software associated with the autonomous operation feature.
15. The computer system of claim 10, further including executable instructions that cause the computer system to: determine that the update has been installed within the autonomous or semi-autonomous vehicle; andadjust an insurance policy associated with the autonomous or semi-autonomous vehicle once the update is installed.
16. A tangible, non-transitory computer-readable medium storing executable instructions for updating an autonomous operation feature that, when executed by at least one processor of a computer system, cause the computer system to: receive an indication of an update associated with the autonomous operation feature for an autonomous or semi-autonomous vehicle having the autonomous operation feature;collect operating data for the autonomous or semi-autonomous vehicle during the operation of the autonomous or semi-autonomous vehicle by electronic communication with an on-board computer of the vehicle, wherein the operating data includes data regarding operation of the autonomous operation feature and environmental conditions during operation of the autonomous or semi-autonomous vehicle;generate at least one risk level associated with the update to the autonomous operation feature based upon virtual or physical testing of the update associated with the autonomous operation feature; anddetermine a weighted risk level for the autonomous or semi-autonomous vehicle, wherein the weighted risk level is determined based upon the at least one risk level and the operating data to facilitate installation of updates to autonomous operation features.
17. The tangible, non-transitory computer-readable medium of claim 16, further including executable instructions that cause the computer system to: determine a risk level change associated with installation of the update for the autonomous or semi-autonomous vehicle;determine whether the risk level change meets one or more risk-related criteria for installing the update; andcause the update to be installed within the autonomous or semi-autonomous vehicle when the risk level change meets the one or more risk-related criteria.
18. The tangible, non-transitory computer-readable medium of claim 17, further including executable instructions that cause the computer system to: determine a change in at least one cost associated with an insurance policy based upon the determined risk level change associated with the autonomous or semi-autonomous vehicle,wherein the one or more risk-related criteria include a reduction in the at least one cost associated with the insurance policy of at least a threshold magnitude, which threshold magnitude is greater than or equal to zero.
19. The tangible, non-transitory computer-readable medium of claim 16, wherein the update comprises an update to a version of software associated with the autonomous operation feature.
20. The tangible, non-transitory computer-readable medium of claim 16, further including executable instructions that cause the computer system to: determine that the update has been installed within the autonomous or semi-autonomous vehicle; andadjust an insurance policy associated with the autonomous or semi-autonomous vehicle after the update has been installed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of, U.S. patent application Ser. No. 15/869,777, filed Jan. 12, 2018 and entitled “Autonomous Vehicle Software Version Assessment,” which is a continuation of U.S. patent application Ser. No. 14/934,347 (now U.S. Pat. No. 9,946,531), filed Nov. 6, 2015, which claims the benefit of U.S. Provisional Application No. 62/079,533 (filed Nov. 13, 2014); U.S. Provisional Application No. 62/103,831 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,836 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,838 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,840 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,855 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,856 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,891 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,893 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,895 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,907 (filed Jan. 15, 2015); U.S. Provisional Application No. 62/103,911 (filed Jan. 15, 2015); and U.S. Provisional Application No. 62/103,914 (filed Jan. 15, 2015). The entirety of each of the foregoing provisional applications is incorporated by reference herein.

US Referenced Citations (991)

Number	Name	Date	Kind
4218763	Kelley et al.	Aug 1980	A
4386376	Takimoto et al.	May 1983	A
4565997	Seko et al.	Jan 1986	A
4833469	David	May 1989	A
5214582	Gray	May 1993	A
5363298	Survanshi et al.	Nov 1994	A
5367456	Summerville et al.	Nov 1994	A
5368484	Copperman et al.	Nov 1994	A
5436839	Dausch et al.	Jul 1995	A
5453939	Hoffman et al.	Sep 1995	A
5488353	Kawakami et al.	Jan 1996	A
5499182	Ousborne	Mar 1996	A
5515026	Ewert	May 1996	A
5574641	Kawakami et al.	Nov 1996	A
5626362	Mottola	May 1997	A
5689241	Clarke, Sr. et al.	Nov 1997	A
5797134	McMillan et al.	Aug 1998	A
5835008	Colemere, Jr.	Nov 1998	A
5983161	Lemelson et al.	Nov 1999	A
6031354	Wiley et al.	Feb 2000	A
6054970	Hirakawa et al.	Apr 2000	A
6064970	McMillan et al.	May 2000	A
6067488	Tano	May 2000	A
6141611	Mackey et al.	Oct 2000	A
6151539	Bergholz et al.	Nov 2000	A
6246933	Bague	Jun 2001	B1
6253129	Jenkins et al.	Jun 2001	B1
6271745	Anzai et al.	Aug 2001	B1
6285931	Hattori et al.	Sep 2001	B1
6298290	Abe et al.	Oct 2001	B1
6313749	Horne et al.	Nov 2001	B1
6323761	Son	Nov 2001	B1
6353396	Atlas	Mar 2002	B1
6400835	Lemelson et al.	Jun 2002	B1
6473000	Secreet et al.	Oct 2002	B1
6477117	Narayanaswami et al.	Nov 2002	B1
6553354	Hausner et al.	Apr 2003	B1
6556905	Mittelsteadt et al.	Apr 2003	B1
6570609	Heien	May 2003	B1
6579233	Hursh	Jun 2003	B2
6661345	Bevan et al.	Dec 2003	B1
6701234	Vogelsang	Mar 2004	B1
6704434	Sakoh et al.	Mar 2004	B1
6727800	Dutu	Apr 2004	B1
6795759	Doyle	Sep 2004	B2
6832141	Skeen et al.	Dec 2004	B2
6889137	Rychlak	May 2005	B1
6909947	Douros et al.	Jun 2005	B2
6934365	Suganuma et al.	Aug 2005	B2
6944536	Singleton	Sep 2005	B2
6983313	Korkea-Aho	Jan 2006	B1
6989737	Yasui	Jan 2006	B2
7027621	Prokoski	Apr 2006	B1
7054723	Seto et al.	May 2006	B2
7102496	Ernst et al.	Sep 2006	B1
7138922	Strumolo et al.	Nov 2006	B2
7149533	Laird et al.	Dec 2006	B2
7253724	Prakah-Asante et al.	Aug 2007	B2
7254482	Kawasaki et al.	Aug 2007	B2
7266532	Sutton et al.	Sep 2007	B2
7290275	Baudoin et al.	Oct 2007	B2
7302344	Olney et al.	Nov 2007	B2
7315233	Yuhara	Jan 2008	B2
7330124	Ota	Feb 2008	B2
7348882	Adamczyk et al.	Mar 2008	B2
7349860	Wallach et al.	Mar 2008	B1
7356392	Hubbard et al.	Apr 2008	B2
7386376	Basir et al.	Jun 2008	B2
7423540	Kisacanin	Sep 2008	B2
7424414	Craft	Sep 2008	B2
7499774	Barrett et al.	Mar 2009	B2
7565230	Gardner et al.	Jul 2009	B2
7596242	Breed et al.	Sep 2009	B2
7609150	Wheatley et al.	Oct 2009	B2
7639148	Victor	Dec 2009	B2
7676062	Breed et al.	Mar 2010	B2
7692552	Harrington et al.	Apr 2010	B2
7719431	Bolourchi	May 2010	B2
7729859	Kimura	Jun 2010	B2
7783426	Kato et al.	Aug 2010	B2
7783505	Roschelle et al.	Aug 2010	B2
7791503	Breed et al.	Sep 2010	B2
7792328	Albertson et al.	Sep 2010	B2
7797107	Shiller	Sep 2010	B2
7812712	White et al.	Oct 2010	B2
7813886	Vian et al.	Oct 2010	B2
7835834	Smith et al.	Nov 2010	B2
7865378	Gay	Jan 2011	B2
7870010	Joao	Jan 2011	B2
7877275	Ball	Jan 2011	B2
7881951	Roschelle et al.	Feb 2011	B2
7890355	Gay et al.	Feb 2011	B2
7899560	Eck	Mar 2011	B2
7904219	Lowrey et al.	Mar 2011	B1
7973674	Bell et al.	Jul 2011	B2
7979172	Breed	Jul 2011	B2
7979173	Breed	Jul 2011	B2
7983802	Breed	Jul 2011	B2
7987103	Gay et al.	Jul 2011	B2
7991629	Gay et al.	Aug 2011	B2
8005467	Gerlach et al.	Aug 2011	B2
8009051	Omi	Aug 2011	B2
8010283	Yoshida et al.	Aug 2011	B2
8016595	Aoki et al.	Sep 2011	B2
8027853	Kazenas	Sep 2011	B1
8035508	Breed	Oct 2011	B2
8040247	Gunaratne	Oct 2011	B2
8068983	Vian et al.	Nov 2011	B2
8078334	Goodrich	Dec 2011	B2
8090596	Bauer et al.	Jan 2012	B2
8095394	Nowak et al.	Jan 2012	B2
8106769	Maroney et al.	Jan 2012	B1
8108655	Abernathy et al.	Jan 2012	B2
8117049	Berkobin et al.	Feb 2012	B2
8123686	Fennell et al.	Feb 2012	B2
8139109	Schmiedel et al.	Mar 2012	B2
8140249	Hessling et al.	Mar 2012	B2
8140358	Ling et al.	Mar 2012	B1
8140359	Daniel	Mar 2012	B2
8164432	Broggi et al.	Apr 2012	B2
8180522	Tuff	May 2012	B2
8180655	Hopkins, III	May 2012	B1
8185380	Kameyama	May 2012	B2
8188887	Catten et al.	May 2012	B2
8190323	Maeda et al.	May 2012	B2
8255144	Breed et al.	Aug 2012	B2
8255243	Raines et al.	Aug 2012	B2
8255244	Raines et al.	Aug 2012	B2
8260489	Nielsen et al.	Sep 2012	B2
8260639	Medina, III et al.	Sep 2012	B1
8265861	Ikeda et al.	Sep 2012	B2
8275417	Flynn	Sep 2012	B2
8280752	Cripe et al.	Oct 2012	B1
8311858	Everett et al.	Nov 2012	B2
8314708	Gunderson et al.	Nov 2012	B2
8332242	Medina, III	Dec 2012	B1
8340893	Yamaguchi et al.	Dec 2012	B2
8340902	Chiang	Dec 2012	B1
8344849	Larsson et al.	Jan 2013	B2
8352118	Mittelsteadt et al.	Jan 2013	B1
8355837	Avery et al.	Jan 2013	B2
8364391	Nagase et al.	Jan 2013	B2
8384534	James et al.	Feb 2013	B2
8385964	Haney	Feb 2013	B2
8386168	Hao	Feb 2013	B2
8423239	Blumer et al.	Apr 2013	B2
8437966	Connolly et al.	May 2013	B2
8447231	Bai et al.	May 2013	B2
8451105	McNay	May 2013	B2
8457880	Malalur et al.	Jun 2013	B1
8473143	Stark et al.	Jun 2013	B2
8487775	Victor et al.	Jul 2013	B2
8520695	Rubin et al.	Aug 2013	B1
8554468	Bullock	Oct 2013	B1
8554587	Nowak et al.	Oct 2013	B1
8566126	Hopkins, III	Oct 2013	B1
8595034	Bauer et al.	Nov 2013	B2
8595037	Hyde et al.	Nov 2013	B1
8605947	Zhang et al.	Dec 2013	B2
8618922	Debouk et al.	Dec 2013	B2
8634980	Urmson et al.	Jan 2014	B1
8645014	Kozlowski et al.	Feb 2014	B1
8645029	Kim et al.	Feb 2014	B2
8660734	Zhu et al.	Feb 2014	B2
8698639	Fung et al.	Apr 2014	B2
8700251	Zhu et al.	Apr 2014	B1
8725311	Breed	May 2014	B1
8725472	Hagelin et al.	May 2014	B2
8731977	Hardin et al.	May 2014	B1
8742936	Galley et al.	Jun 2014	B2
8781442	Link, II	Jul 2014	B1
8781669	Teller et al.	Jul 2014	B1
8788299	Medina, III	Jul 2014	B1
8799034	Brandmaier et al.	Aug 2014	B1
8816836	Lee et al.	Aug 2014	B2
8818608	Cullinane et al.	Aug 2014	B2
8825258	Cullinane et al.	Sep 2014	B2
8849558	Morotomi et al.	Sep 2014	B2
8868288	Plante et al.	Oct 2014	B2
8874301	Rao et al.	Oct 2014	B1
8874305	Dolgov et al.	Oct 2014	B2
8876535	Fields et al.	Nov 2014	B2
8880291	Hampiholi	Nov 2014	B2
8892271	Breed	Nov 2014	B2
8902054	Morris	Dec 2014	B2
8909428	Lombrozo	Dec 2014	B1
8917182	Chang et al.	Dec 2014	B2
8928495	Hassib et al.	Jan 2015	B2
8935036	Christensen et al.	Jan 2015	B1
8954205	Sagar et al.	Feb 2015	B2
8954217	Montemerlo et al.	Feb 2015	B1
8954226	Binion et al.	Feb 2015	B1
8965677	Breed et al.	Feb 2015	B2
8972100	Mullen et al.	Mar 2015	B2
8989959	Plante et al.	Mar 2015	B2
8996228	Ferguson et al.	Mar 2015	B1
8996240	Plante	Mar 2015	B2
9008952	Caskey et al.	Apr 2015	B2
9019092	Brandmaier et al.	Apr 2015	B1
9020876	Rakshit	Apr 2015	B2
9049584	Hatton	Jun 2015	B2
9053588	Briggs et al.	Jun 2015	B1
9056395	Ferguson et al.	Jun 2015	B1
9063543	An et al.	Jun 2015	B2
9070243	Kozlowski et al.	Jun 2015	B1
9075413	Cullinane et al.	Jul 2015	B2
9079587	Rupp et al.	Jul 2015	B1
9081650	Brinkmann et al.	Jul 2015	B1
9098080	Norris et al.	Aug 2015	B2
9123250	Duncan et al.	Sep 2015	B2
9135803	Fields et al.	Sep 2015	B1
9141996	Christensen et al.	Sep 2015	B2
9144389	Srinivasan et al.	Sep 2015	B2
9147219	Binion et al.	Sep 2015	B2
9147353	Slusar	Sep 2015	B1
9151692	Breed	Oct 2015	B2
9157752	Fernandez et al.	Oct 2015	B1
9164507	Cheatham, III et al.	Oct 2015	B2
9177475	Sellschopp	Nov 2015	B2
9182942	Kelly et al.	Nov 2015	B2
9188985	Hobbs et al.	Nov 2015	B1
9194168	Lu et al.	Nov 2015	B1
9205805	Cudak et al.	Dec 2015	B2
9205842	Fields et al.	Dec 2015	B1
9221395	Honig et al.	Dec 2015	B2
9221396	Zhu et al.	Dec 2015	B1
9224293	Taylor	Dec 2015	B2
9235211	Davidsson et al.	Jan 2016	B2
9262787	Binion et al.	Feb 2016	B2
9274525	Ferguson et al.	Mar 2016	B1
9275417	Binion et al.	Mar 2016	B2
9275552	Fields et al.	Mar 2016	B1
9282430	Brandmaier et al.	Mar 2016	B1
9282447	Gianakis	Mar 2016	B2
9299108	Diana et al.	Mar 2016	B2
9308891	Cudak et al.	Apr 2016	B2
9311271	Wright	Apr 2016	B2
9317983	Ricci	Apr 2016	B2
9342074	Dolgov et al.	May 2016	B2
9342993	Fields et al.	May 2016	B1
9352709	Brenneis et al.	May 2016	B2
9352752	Cullinane et al.	May 2016	B2
9355423	Slusar	May 2016	B1
9361599	Biemer et al.	Jun 2016	B1
9361650	Binion et al.	Jun 2016	B2
9371072	Sisbot	Jun 2016	B1
9376090	Gennermann	Jun 2016	B2
9377315	Grover et al.	Jun 2016	B2
9381916	Zhu et al.	Jul 2016	B1
9384491	Briggs et al.	Jul 2016	B1
9390451	Slusar	Jul 2016	B1
9390452	Biemer et al.	Jul 2016	B1
9390567	Kim et al.	Jul 2016	B2
9399445	Abou Mahmoud et al.	Jul 2016	B2
9406177	Attard et al.	Aug 2016	B2
9421972	Davidsson et al.	Aug 2016	B2
9424607	Bowers et al.	Aug 2016	B2
9429943	Wilson et al.	Aug 2016	B2
9430944	Grimm et al.	Aug 2016	B2
9440657	Fields et al.	Sep 2016	B1
9443152	Atsmon et al.	Sep 2016	B2
9443436	Scheidt	Sep 2016	B2
9454786	Srey et al.	Sep 2016	B1
9466214	Fuehrer	Oct 2016	B2
9475496	Attard et al.	Oct 2016	B2
9477990	Binion et al.	Oct 2016	B1
9478150	Fields et al.	Oct 2016	B1
9489635	Zhu	Nov 2016	B1
9505494	Marlow et al.	Nov 2016	B1
9511765	Obradovich	Dec 2016	B2
9511767	Okumura et al.	Dec 2016	B1
9511779	Cullinane et al.	Dec 2016	B2
9517771	Attard et al.	Dec 2016	B2
9524648	Gopalakrishnan et al.	Dec 2016	B1
9529361	You et al.	Dec 2016	B2
9530333	Fields et al.	Dec 2016	B1
9542846	Zeng et al.	Jan 2017	B2
9558520	Peak	Jan 2017	B2
9558667	Bowers et al.	Jan 2017	B2
9566959	Breuer et al.	Feb 2017	B2
9567007	Cudak et al.	Feb 2017	B2
9587952	Slusar	Mar 2017	B1
9594373	Solyom et al.	Mar 2017	B2
9604652	Strauss	Mar 2017	B2
9632502	Levinson et al.	Apr 2017	B1
9633318	Plante	Apr 2017	B2
9646428	Konrardy et al.	May 2017	B1
9650051	Hoye et al.	May 2017	B2
9656606	Vose et al.	May 2017	B1
9663112	Abou-Nasr et al.	May 2017	B2
9665101	Templeton	May 2017	B1
9679487	Hayward	Jun 2017	B1
9697733	Penilla et al.	Jul 2017	B1
9707942	Cheatham, III et al.	Jul 2017	B2
9712549	Almurayh	Jul 2017	B2
9715711	Konrardy et al.	Jul 2017	B1
9720419	O'Neill et al.	Aug 2017	B2
9725036	Tarte	Aug 2017	B1
9727920	Healy et al.	Aug 2017	B1
9734685	Fields et al.	Aug 2017	B2
9753390	Kabai	Sep 2017	B2
9754325	Konrardy et al.	Sep 2017	B1
9754424	Ling et al.	Sep 2017	B2
9754490	Kentley et al.	Sep 2017	B2
9760702	Kursun et al.	Sep 2017	B1
9761139	Acker et al.	Sep 2017	B2
9766625	Boroditsky et al.	Sep 2017	B2
9767516	Konrardy et al.	Sep 2017	B1
9773281	Hanson	Sep 2017	B1
9792656	Konrardy et al.	Oct 2017	B1
9805423	Konrardy et al.	Oct 2017	B1
9805601	Fields et al.	Oct 2017	B1
9816827	Slusar	Nov 2017	B1
9817400	Poeppel et al.	Nov 2017	B1
9847033	Carmack et al.	Dec 2017	B1
9852475	Konrardy et al.	Dec 2017	B1
9858621	Konrardy et al.	Jan 2018	B1
9868394	Fields et al.	Jan 2018	B1
9870649	Fields et al.	Jan 2018	B1
9884611	Abou Mahmoud et al.	Feb 2018	B2
9892567	Binion et al.	Feb 2018	B2
9904928	Leise	Feb 2018	B1
9932033	Slusar	Apr 2018	B2
9939279	Pan et al.	Apr 2018	B2
9940676	Biemer	Apr 2018	B1
9940834	Konrardy et al.	Apr 2018	B1
9944282	Fields et al.	Apr 2018	B1
9946531	Fields et al.	Apr 2018	B1
9948477	Marten	Apr 2018	B2
9972054	Konrardy et al.	May 2018	B1
9986404	Mehta et al.	May 2018	B2
9995584	Kanevsky	Jun 2018	B1
10013697	Cote et al.	Jul 2018	B1
10019901	Fields et al.	Jul 2018	B1
10026130	Konrardy et al.	Jul 2018	B1
10026237	Fields et al.	Jul 2018	B1
10042359	Konrardy et al.	Aug 2018	B1
10043323	Konrardy et al.	Aug 2018	B1
10049505	Harvey et al.	Aug 2018	B1
10055794	Konrardy et al.	Aug 2018	B1
10065517	Konrardy et al.	Sep 2018	B1
10086782	Konrardy et al.	Oct 2018	B1
10089693	Konrardy et al.	Oct 2018	B1
10102586	Marlow et al.	Oct 2018	B1
10102590	Farnsworth et al.	Oct 2018	B1
10106083	Fields et al.	Oct 2018	B1
10134278	Konrardy et al.	Nov 2018	B1
10156848	Konrardy et al.	Dec 2018	B1
10163350	Fields et al.	Dec 2018	B1
10168703	Konrardy et al.	Jan 2019	B1
10181161	Konrardy et al.	Jan 2019	B1
10185997	Konrardy et al.	Jan 2019	B1
10185998	Konrardy et al.	Jan 2019	B1
10185999	Konrardy et al.	Jan 2019	B1
10300926	Cullinane	May 2019	B2
10354330	Konrardy et al.	Jul 2019	B1
10373257	Iqbal et al.	Aug 2019	B1
10373259	Konrardy et al.	Aug 2019	B1
10373265	Konrardy et al.	Aug 2019	B1
10399493	Adams	Sep 2019	B2
20010005217	Hamilton et al.	Jun 2001	A1
20020016655	Joao	Feb 2002	A1
20020049535	Rigo et al.	Apr 2002	A1
20020091483	Douet	Jul 2002	A1
20020103622	Burge	Aug 2002	A1
20020103678	Burkhalter et al.	Aug 2002	A1
20020111725	Burge	Aug 2002	A1
20020116228	Bauer et al.	Aug 2002	A1
20020128751	Engstrom et al.	Sep 2002	A1
20020128882	Nakagawa et al.	Sep 2002	A1
20020135618	Maes et al.	Sep 2002	A1
20020146667	Dowdell et al.	Oct 2002	A1
20020169535	Imai et al.	Nov 2002	A1
20030028298	Macky et al.	Feb 2003	A1
20030061160	Asahina	Mar 2003	A1
20030095039	Shimomura et al.	May 2003	A1
20030112133	Webb et al.	Jun 2003	A1
20030139948	Strech	Jul 2003	A1
20030146850	Fallenstein	Aug 2003	A1
20030182042	Watson et al.	Sep 2003	A1
20030182183	Pribe	Sep 2003	A1
20030200123	Burge et al.	Oct 2003	A1
20030229528	Nitao et al.	Dec 2003	A1
20040005927	Bonilla et al.	Jan 2004	A1
20040017106	Aizawa et al.	Jan 2004	A1
20040019539	Raman et al.	Jan 2004	A1
20040039503	Doyle	Feb 2004	A1
20040054452	Bjorkman	Mar 2004	A1
20040077285	Bonilla et al.	Apr 2004	A1
20040085198	Saito et al.	May 2004	A1
20040090334	Zhang et al.	May 2004	A1
20040111301	Wahlbin et al.	Jun 2004	A1
20040122639	Qiu	Jun 2004	A1
20040139034	Farmer	Jul 2004	A1
20040153362	Bauer et al.	Aug 2004	A1
20040158476	Blessinger et al.	Aug 2004	A1
20040169034	Park	Sep 2004	A1
20040198441	Cooper et al.	Oct 2004	A1
20040204837	Singleton	Oct 2004	A1
20040226043	Mettu et al.	Nov 2004	A1
20040252027	Torkkola et al.	Dec 2004	A1
20040260579	Tremiti	Dec 2004	A1
20050007438	Busch et al.	Jan 2005	A1
20050046584	Breed	Mar 2005	A1
20050055249	Helitzer et al.	Mar 2005	A1
20050059151	Bosch	Mar 2005	A1
20050065678	Smith et al.	Mar 2005	A1
20050071052	Coletrane et al.	Mar 2005	A1
20050071202	Kendrick	Mar 2005	A1
20050073438	Rodgers et al.	Apr 2005	A1
20050080519	Oesterling et al.	Apr 2005	A1
20050088291	Blanco et al.	Apr 2005	A1
20050088521	Blanco et al.	Apr 2005	A1
20050093684	Cunnien	May 2005	A1
20050107673	Ball	May 2005	A1
20050108910	Esparza et al.	May 2005	A1
20050131597	Raz et al.	Jun 2005	A1
20050154513	Matsunaga et al.	Jul 2005	A1
20050216136	Lengning et al.	Sep 2005	A1
20050228763	Lewis et al.	Oct 2005	A1
20050237784	Kang	Oct 2005	A1
20050246256	Gastineau et al.	Nov 2005	A1
20050259151	Hamilton et al.	Nov 2005	A1
20050267784	Slen et al.	Dec 2005	A1
20060031103	Henry	Feb 2006	A1
20060052909	Cherouny	Mar 2006	A1
20060052929	Bastian et al.	Mar 2006	A1
20060053038	Warren et al.	Mar 2006	A1
20060055565	Kawamata et al.	Mar 2006	A1
20060079260	LaPerch	Apr 2006	A1
20060089763	Barrett et al.	Apr 2006	A1
20060089766	Allard et al.	Apr 2006	A1
20060092043	Lagassey	May 2006	A1
20060136291	Morita et al.	Jun 2006	A1
20060149461	Rowley et al.	Jul 2006	A1
20060184295	Hawkins et al.	Aug 2006	A1
20060212195	Veith et al.	Sep 2006	A1
20060220905	Hovestadt	Oct 2006	A1
20060229777	Hudson et al.	Oct 2006	A1
20060232430	Takaoka et al.	Oct 2006	A1
20060294514	Bauchot et al.	Dec 2006	A1
20070001831	Raz et al.	Jan 2007	A1
20070027726	Warren et al.	Feb 2007	A1
20070048707	Caamano et al.	Mar 2007	A1
20070055422	Anzai et al.	Mar 2007	A1
20070080816	Haque et al.	Apr 2007	A1
20070088469	Schmiedel et al.	Apr 2007	A1
20070093947	Gould et al.	Apr 2007	A1
20070122771	Maeda et al.	May 2007	A1
20070124599	Morita et al.	May 2007	A1
20070132773	Plante	Jun 2007	A1
20070149208	Syrbe et al.	Jun 2007	A1
20070159344	Kisacanin	Jul 2007	A1
20070159354	Rosenberg	Jul 2007	A1
20070208498	Barker et al.	Sep 2007	A1
20070219720	Trepagnier et al.	Sep 2007	A1
20070265540	Fuwamoto et al.	Nov 2007	A1
20070282489	Boss et al.	Dec 2007	A1
20070282638	Surovy	Dec 2007	A1
20070291130	Broggi et al.	Dec 2007	A1
20070299700	Gay et al.	Dec 2007	A1
20080027761	Bracha	Jan 2008	A1
20080028974	Bianco	Feb 2008	A1
20080033684	Vian et al.	Feb 2008	A1
20080052134	Nowak et al.	Feb 2008	A1
20080061953	Bhogal et al.	Mar 2008	A1
20080064014	Wojtczak et al.	Mar 2008	A1
20080065427	Helitzer et al.	Mar 2008	A1
20080077383	Hagelin et al.	Mar 2008	A1
20080082372	Burch	Apr 2008	A1
20080084473	Romanowich	Apr 2008	A1
20080106390	White	May 2008	A1
20080111666	Plante et al.	May 2008	A1
20080114502	Breed et al.	May 2008	A1
20080114530	Petrisor et al.	May 2008	A1
20080126137	Kidd et al.	May 2008	A1
20080143497	Wasson et al.	Jun 2008	A1
20080147265	Breed	Jun 2008	A1
20080147266	Plante et al.	Jun 2008	A1
20080147267	Plante et al.	Jun 2008	A1
20080161989	Breed	Jul 2008	A1
20080167821	Breed	Jul 2008	A1
20080180237	Fayyad et al.	Jul 2008	A1
20080189142	Brown et al.	Aug 2008	A1
20080204256	Omi	Aug 2008	A1
20080255887	Gruter	Oct 2008	A1
20080255888	Berkobin et al.	Oct 2008	A1
20080258885	Akhan	Oct 2008	A1
20080258890	Follmer et al.	Oct 2008	A1
20080291008	Jeon	Nov 2008	A1
20080294302	Basir	Nov 2008	A1
20080294690	McClellan et al.	Nov 2008	A1
20080297488	Operowsky et al.	Dec 2008	A1
20080300733	Rasshofer et al.	Dec 2008	A1
20080313007	Callahan et al.	Dec 2008	A1
20080319665	Berkobin et al.	Dec 2008	A1
20080319817	Simon	Dec 2008	A1
20090005979	Nakao et al.	Jan 2009	A1
20090015684	Ooga et al.	Jan 2009	A1
20090027188	Saban	Jan 2009	A1
20090063030	Howarter et al.	Mar 2009	A1
20090069953	Hale et al.	Mar 2009	A1
20090079839	Fischer et al.	Mar 2009	A1
20090081923	Dooley et al.	Mar 2009	A1
20090085770	Mergen	Apr 2009	A1
20090106135	Steiger	Apr 2009	A1
20090109037	Farmer	Apr 2009	A1
20090115638	Shankwitz et al.	May 2009	A1
20090132294	Haines	May 2009	A1
20090140887	Breed et al.	Jun 2009	A1
20090174573	Smith	Jul 2009	A1
20090207005	Habetha et al.	Aug 2009	A1
20090210257	Chalfant et al.	Aug 2009	A1
20090254240	Olsen et al.	Oct 2009	A1
20090267801	Kawai et al.	Oct 2009	A1
20090300065	Birchall	Dec 2009	A1
20090303026	Broggi et al.	Dec 2009	A1
20090313566	Vian et al.	Dec 2009	A1
20100004995	Hickman	Jan 2010	A1
20100030540	Choi et al.	Feb 2010	A1
20100030586	Taylor et al.	Feb 2010	A1
20100042318	Kaplan et al.	Feb 2010	A1
20100055649	Takahashi et al.	Mar 2010	A1
20100076646	Basir et al.	Mar 2010	A1
20100085171	Do	Apr 2010	A1
20100106346	Badli et al.	Apr 2010	A1
20100106356	Trepagnier et al.	Apr 2010	A1
20100106514	Cox	Apr 2010	A1
20100128127	Ciolli	May 2010	A1
20100131300	Collopy et al.	May 2010	A1
20100131302	Collopy et al.	May 2010	A1
20100131304	Collopy et al.	May 2010	A1
20100131307	Collopy et al.	May 2010	A1
20100143872	Lankteee	Jun 2010	A1
20100157255	Togino	Jun 2010	A1
20100164737	Lu et al.	Jul 2010	A1
20100198491	Mays	Aug 2010	A1
20100213884	Xiang	Aug 2010	A1
20100214087	Nakagoshi et al.	Aug 2010	A1
20100219944	Mc Cormick et al.	Sep 2010	A1
20100228419	Lee	Sep 2010	A1
20100253541	Seder et al.	Oct 2010	A1
20100256836	Mudalige	Oct 2010	A1
20100286845	Rekow et al.	Nov 2010	A1
20100293033	Hall et al.	Nov 2010	A1
20100299021	Jalili	Nov 2010	A1
20110009093	Self et al.	Jan 2011	A1
20110010042	Boulet et al.	Jan 2011	A1
20110043350	Ben David	Feb 2011	A1
20110043377	McGrath et al.	Feb 2011	A1
20110054767	Schafer et al.	Mar 2011	A1
20110060496	Nielsen et al.	Mar 2011	A1
20110066310	Sakai et al.	Mar 2011	A1
20110077809	Leary	Mar 2011	A1
20110087505	Terlep	Apr 2011	A1
20110090075	Armitage et al.	Apr 2011	A1
20110090093	Grimm et al.	Apr 2011	A1
20110093134	Emanuel et al.	Apr 2011	A1
20110093350	Laumeyer et al.	Apr 2011	A1
20110106370	Duddle et al.	May 2011	A1
20110109462	Deng et al.	May 2011	A1
20110117878	Barash et al.	May 2011	A1
20110118907	Elkins	May 2011	A1
20110128161	Bae et al.	Jun 2011	A1
20110133954	Ooshima et al.	Jun 2011	A1
20110137684	Peak et al.	Jun 2011	A1
20110140919	Hara et al.	Jun 2011	A1
20110140968	Bai et al.	Jun 2011	A1
20110144854	Cramer et al.	Jun 2011	A1
20110153367	Amigo et al.	Jun 2011	A1
20110161116	Peak et al.	Jun 2011	A1
20110161119	Collins	Jun 2011	A1
20110169625	James et al.	Jul 2011	A1
20110184605	Neff	Jul 2011	A1
20110187559	Applebaum	Aug 2011	A1
20110190972	Timmons et al.	Aug 2011	A1
20110196571	Foladare et al.	Aug 2011	A1
20110202305	Willis et al.	Aug 2011	A1
20110241862	Debouk et al.	Oct 2011	A1
20110251751	Knight	Oct 2011	A1
20110279263	Rodkey et al.	Nov 2011	A1
20110288770	Greasby	Nov 2011	A1
20110295446	Basir et al.	Dec 2011	A1
20110295546	Khazanov	Dec 2011	A1
20110301839	Pudar et al.	Dec 2011	A1
20110304465	Boult et al.	Dec 2011	A1
20110307188	Peng et al.	Dec 2011	A1
20110307336	Smirnov et al.	Dec 2011	A1
20120004933	Foladare et al.	Jan 2012	A1
20120010906	Foladare et al.	Jan 2012	A1
20120013582	Inoue et al.	Jan 2012	A1
20120019001	Hede et al.	Jan 2012	A1
20120025969	Dozza	Feb 2012	A1
20120028680	Breed	Feb 2012	A1
20120053824	Nam et al.	Mar 2012	A1
20120056758	Kuhlman et al.	Mar 2012	A1
20120059227	Friedlander et al.	Mar 2012	A1
20120062392	Ferrick et al.	Mar 2012	A1
20120066007	Ferrick et al.	Mar 2012	A1
20120071151	Abramson et al.	Mar 2012	A1
20120072214	Cox et al.	Mar 2012	A1
20120072243	Collins et al.	Mar 2012	A1
20120072244	Collins et al.	Mar 2012	A1
20120083668	Pradeep et al.	Apr 2012	A1
20120083959	Dolgov et al.	Apr 2012	A1
20120083960	Zhu et al.	Apr 2012	A1
20120083964	Montemerlo et al.	Apr 2012	A1
20120083974	Sandblom	Apr 2012	A1
20120092157	Tran	Apr 2012	A1
20120101855	Collins et al.	Apr 2012	A1
20120108909	Slobounov et al.	May 2012	A1
20120109407	Yousefi et al.	May 2012	A1
20120109692	Collins et al.	May 2012	A1
20120123806	Schumann, Jr.	May 2012	A1
20120135382	Winston et al.	May 2012	A1
20120143391	Gee	Jun 2012	A1
20120143630	Hertenstein	Jun 2012	A1
20120172055	Edge	Jul 2012	A1
20120185204	Jallon et al.	Jul 2012	A1
20120188100	Min et al.	Jul 2012	A1
20120190001	Knight et al.	Jul 2012	A1
20120191343	Haleem	Jul 2012	A1
20120191373	Soles et al.	Jul 2012	A1
20120197669	Kote et al.	Aug 2012	A1
20120200427	Kamata	Aug 2012	A1
20120203418	Braennstroem et al.	Aug 2012	A1
20120209634	Ling et al.	Aug 2012	A1
20120209692	Bennett et al.	Aug 2012	A1
20120215375	Chang	Aug 2012	A1
20120221168	Zeng et al.	Aug 2012	A1
20120235865	Nath et al.	Sep 2012	A1
20120239242	Uehara	Sep 2012	A1
20120239281	Hinz	Sep 2012	A1
20120239471	Grimm et al.	Sep 2012	A1
20120246733	Schafer et al.	Sep 2012	A1
20120256769	Satpathy	Oct 2012	A1
20120258702	Matsuyama	Oct 2012	A1
20120271500	Tsimhoni et al.	Oct 2012	A1
20120277950	Plante et al.	Nov 2012	A1
20120286974	Claussen et al.	Nov 2012	A1
20120289819	Snow	Nov 2012	A1
20120303177	Jauch et al.	Nov 2012	A1
20120303222	Cooprider et al.	Nov 2012	A1
20120306663	Mudalige	Dec 2012	A1
20120316406	Rahman et al.	Dec 2012	A1
20130006674	Bowne et al.	Jan 2013	A1
20130006675	Bowne et al.	Jan 2013	A1
20130013348	Ling et al.	Jan 2013	A1
20130018677	Chevrette	Jan 2013	A1
20130030606	Mudalige et al.	Jan 2013	A1
20130038437	Talati et al.	Feb 2013	A1
20130044008	Gafford et al.	Feb 2013	A1
20130046562	Taylor et al.	Feb 2013	A1
20130066751	Glazer et al.	Mar 2013	A1
20130073115	Levin et al.	Mar 2013	A1
20130097128	Suzuki et al.	Apr 2013	A1
20130116855	Nielsen et al.	May 2013	A1
20130131907	Green et al.	May 2013	A1
20130144459	Ricci	Jun 2013	A1
20130151027	Petrucci et al.	Jun 2013	A1
20130151202	Denny et al.	Jun 2013	A1
20130164715	Hunt et al.	Jun 2013	A1
20130179198	Bowne et al.	Jul 2013	A1
20130189649	Mannino	Jul 2013	A1
20130190966	Collins et al.	Jul 2013	A1
20130204645	Lehman et al.	Aug 2013	A1
20130209968	Miller et al.	Aug 2013	A1
20130218603	Hagelstein et al.	Aug 2013	A1
20130218604	Hagelstein et al.	Aug 2013	A1
20130226391	Nordbruch et al.	Aug 2013	A1
20130226624	Blessman et al.	Aug 2013	A1
20130227409	Das et al.	Aug 2013	A1
20130231824	Wilson et al.	Sep 2013	A1
20130237194	Davis	Sep 2013	A1
20130245857	Gariepy et al.	Sep 2013	A1
20130245881	Scarbrough	Sep 2013	A1
20130257626	Masli et al.	Oct 2013	A1
20130267194	Breed	Oct 2013	A1
20130278442	Rubin et al.	Oct 2013	A1
20130289819	Hassib et al.	Oct 2013	A1
20130302758	Wright	Nov 2013	A1
20130304513	Hyde et al.	Nov 2013	A1
20130304514	Hyde et al.	Nov 2013	A1
20130307786	Heubel	Nov 2013	A1
20130317693	Jefferies et al.	Nov 2013	A1
20130317711	Plante	Nov 2013	A1
20130317786	Kuhn	Nov 2013	A1
20130317865	Tofte et al.	Nov 2013	A1
20130332402	Rakshit	Dec 2013	A1
20130339062	Brewer et al.	Dec 2013	A1
20140002651	Plante	Jan 2014	A1
20140004734	Hoang	Jan 2014	A1
20140006660	Frei et al.	Jan 2014	A1
20140009307	Bowers et al.	Jan 2014	A1
20140012492	Bowers et al.	Jan 2014	A1
20140019170	Coleman et al.	Jan 2014	A1
20140039934	Rivera	Feb 2014	A1
20140047347	Mohn et al.	Feb 2014	A1
20140047371	Palmer et al.	Feb 2014	A1
20140052323	Reichel et al.	Feb 2014	A1
20140052336	Moshchuk et al.	Feb 2014	A1
20140052479	Kawamura	Feb 2014	A1
20140058761	Freiberger et al.	Feb 2014	A1
20140059066	Koloskov	Feb 2014	A1
20140070980	Park	Mar 2014	A1
20140080100	Phelan et al.	Mar 2014	A1
20140095009	Oshima et al.	Apr 2014	A1
20140095214	Mathe et al.	Apr 2014	A1
20140099607	Armitage et al.	Apr 2014	A1
20140100892	Collopy et al.	Apr 2014	A1
20140104405	Weidl et al.	Apr 2014	A1
20140106782	Chitre et al.	Apr 2014	A1
20140108198	Jariyasunant et al.	Apr 2014	A1
20140111332	Przybylko et al.	Apr 2014	A1
20140114691	Pearce	Apr 2014	A1
20140125474	Gunaratne	May 2014	A1
20140129053	Kleve et al.	May 2014	A1
20140129301	Van Wiemeersch et al.	May 2014	A1
20140130035	Desai et al.	May 2014	A1
20140135598	Weidl et al.	May 2014	A1
20140136045	Zhu et al.	May 2014	A1
20140148988	Lathrop et al.	May 2014	A1
20140149148	Luciani	May 2014	A1
20140152422	Breed	Jun 2014	A1
20140156133	Cullinane et al.	Jun 2014	A1
20140156134	Cullinane et al.	Jun 2014	A1
20140156176	Caskey et al.	Jun 2014	A1
20140167967	He et al.	Jun 2014	A1
20140168399	Plummer et al.	Jun 2014	A1
20140172467	He et al.	Jun 2014	A1
20140172727	Abhyanker et al.	Jun 2014	A1
20140188322	Oh	Jul 2014	A1
20140191858	Morgan et al.	Jul 2014	A1
20140207707	Na et al.	Jul 2014	A1
20140218187	Chun et al.	Aug 2014	A1
20140218520	Teich et al.	Aug 2014	A1
20140221781	Schrauf et al.	Aug 2014	A1
20140236638	Pallesen et al.	Aug 2014	A1
20140240132	Bychkov	Aug 2014	A1
20140244096	An et al.	Aug 2014	A1
20140253376	Large et al.	Sep 2014	A1
20140257866	Gay et al.	Sep 2014	A1
20140266655	Palan	Sep 2014	A1
20140272810	Fields et al.	Sep 2014	A1
20140272811	Palan	Sep 2014	A1
20140277916	Mullen et al.	Sep 2014	A1
20140278571	Mullen et al.	Sep 2014	A1
20140278840	Scofield et al.	Sep 2014	A1
20140279707	Joshua et al.	Sep 2014	A1
20140301218	Luo et al.	Oct 2014	A1
20140303827	Dolgov et al.	Oct 2014	A1
20140306799	Ricci	Oct 2014	A1
20140306814	Ricci	Oct 2014	A1
20140309864	Ricci	Oct 2014	A1
20140309870	Ricci et al.	Oct 2014	A1
20140310186	Ricci	Oct 2014	A1
20140330478	Cullinane et al.	Nov 2014	A1
20140337930	Hoyos et al.	Nov 2014	A1
20140343972	Fernandes et al.	Nov 2014	A1
20140350970	Schumann, Jr. et al.	Nov 2014	A1
20140358324	Sagar et al.	Dec 2014	A1
20140358592	Wedig et al.	Dec 2014	A1
20140380264	Misra et al.	Dec 2014	A1
20150006278	Di Censo et al.	Jan 2015	A1
20150019266	Stempora	Jan 2015	A1
20150024705	Rashidi	Jan 2015	A1
20150025917	Stempora	Jan 2015	A1
20150032581	Blackhurst et al.	Jan 2015	A1
20150035685	Strickland et al.	Feb 2015	A1
20150039350	Martin et al.	Feb 2015	A1
20150039397	Fuchs	Feb 2015	A1
20150045983	Fraser et al.	Feb 2015	A1
20150051752	Paszkowicz	Feb 2015	A1
20150051787	Doughty et al.	Feb 2015	A1
20150066284	Yopp	Mar 2015	A1
20150070160	Davidsson et al.	Mar 2015	A1
20150070265	Cruz-Hernandez et al.	Mar 2015	A1
20150073645	Davidsson et al.	Mar 2015	A1
20150088334	Bowers et al.	Mar 2015	A1
20150088358	Yopp	Mar 2015	A1
20150088360	Bonnet et al.	Mar 2015	A1
20150088373	Wilkins	Mar 2015	A1
20150088550	Bowers et al.	Mar 2015	A1
20150100189	Tellis et al.	Apr 2015	A1
20150100190	Yopp	Apr 2015	A1
20150100191	Yopp	Apr 2015	A1
20150109450	Walker	Apr 2015	A1
20150112504	Binion et al.	Apr 2015	A1
20150112543	Binion et al.	Apr 2015	A1
20150112545	Binion et al.	Apr 2015	A1
20150112730	Binion et al.	Apr 2015	A1
20150112731	Binion et al.	Apr 2015	A1
20150112800	Binion et al.	Apr 2015	A1
20150113521	Suzuki	Apr 2015	A1
20150120331	Russo et al.	Apr 2015	A1
20150127570	Doughty et al.	May 2015	A1
20150128123	Eling	May 2015	A1
20150142244	You et al.	May 2015	A1
20150142262	Lee	May 2015	A1
20150149017	Attard et al.	May 2015	A1
20150149018	Attard et al.	May 2015	A1
20150149023	Attard et al.	May 2015	A1
20150149265	Huntzicker et al.	May 2015	A1
20150153733	Ohmura et al.	Jun 2015	A1
20150158469	Cheatham, III et al.	Jun 2015	A1
20150158495	Duncan et al.	Jun 2015	A1
20150160653	Cheatham, III et al.	Jun 2015	A1
20150161564	Sweeney et al.	Jun 2015	A1
20150161738	Stempora	Jun 2015	A1
20150161893	Duncan et al.	Jun 2015	A1
20150161894	Duncan et al.	Jun 2015	A1
20150166069	Engelman et al.	Jun 2015	A1
20150169311	Dickerson	Jun 2015	A1
20150170287	Tirone	Jun 2015	A1
20150170290	Bowne et al.	Jun 2015	A1
20150170522	Noh	Jun 2015	A1
20150178997	Ohsaki	Jun 2015	A1
20150178998	Attard et al.	Jun 2015	A1
20150185034	Abhyanker	Jul 2015	A1
20150187013	Adams et al.	Jul 2015	A1
20150187015	Adams et al.	Jul 2015	A1
20150187016	Adams et al.	Jul 2015	A1
20150187019	Fernandes	Jul 2015	A1
20150187194	Hypolite et al.	Jul 2015	A1
20150189241	Kim et al.	Jul 2015	A1
20150193219	Pandya et al.	Jul 2015	A1
20150193220	Rork	Jul 2015	A1
20150203107	Lippman	Jul 2015	A1
20150203113	Duncan et al.	Jul 2015	A1
20150221142	Kim et al.	Aug 2015	A1
20150229885	Offenhaeuser	Aug 2015	A1
20150232064	Cudak et al.	Aug 2015	A1
20150233719	Cudak et al.	Aug 2015	A1
20150235323	Oldham	Aug 2015	A1
20150235480	Cudak	Aug 2015	A1
20150235557	Engelman et al.	Aug 2015	A1
20150239436	Kanai et al.	Aug 2015	A1
20150241241	Cudak et al.	Aug 2015	A1
20150241853	Vechart et al.	Aug 2015	A1
20150242953	Suiter	Aug 2015	A1
20150246672	Pilutti et al.	Sep 2015	A1
20150253772	Solyom et al.	Sep 2015	A1
20150254955	Fields et al.	Sep 2015	A1
20150266489	Solyom et al.	Sep 2015	A1
20150266490	Coelingh et al.	Sep 2015	A1
20150271201	Ruvio et al.	Sep 2015	A1
20150274072	Croteau et al.	Oct 2015	A1
20150276415	Shrinath et al.	Oct 2015	A1
20150284009	Cullinane et al.	Oct 2015	A1
20150293534	Takamatsu	Oct 2015	A1
20150294422	Carver	Oct 2015	A1
20150307110	Grewe et al.	Oct 2015	A1
20150310742	Albornoz	Oct 2015	A1
20150310758	Daddona et al.	Oct 2015	A1
20150314780	Stenneth	Nov 2015	A1
20150321641	Abou Mahmoud et al.	Nov 2015	A1
20150332407	Wilson, II et al.	Nov 2015	A1
20150334545	Maier et al.	Nov 2015	A1
20150336502	Hillis et al.	Nov 2015	A1
20150338227	Kruecken	Nov 2015	A1
20150338852	Ramanujam	Nov 2015	A1
20150339928	Ramanujam	Nov 2015	A1
20150343947	Bernico et al.	Dec 2015	A1
20150346727	Ramanujam	Dec 2015	A1
20150348335	Ramanujam	Dec 2015	A1
20150348337	Choi	Dec 2015	A1
20150356797	McBride et al.	Dec 2015	A1
20150382085	Lawrie-Fussey et al.	Dec 2015	A1
20160019790	Tobolski et al.	Jan 2016	A1
20160026182	Boroditsky et al.	Jan 2016	A1
20160027276	Freeck et al.	Jan 2016	A1
20160036899	Moody et al.	Feb 2016	A1
20160042463	Gillespie	Feb 2016	A1
20160042644	Velusamy	Feb 2016	A1
20160042650	Stenneth	Feb 2016	A1
20160055750	Linder et al.	Feb 2016	A1
20160059825	Coombs	Mar 2016	A1
20160068103	McNew et al.	Mar 2016	A1
20160071418	Oshida et al.	Mar 2016	A1
20160083285	De et al.	Mar 2016	A1
20160086285	Jordan Peters et al.	Mar 2016	A1
20160086393	Collins et al.	Mar 2016	A1
20160092962	Wasserman et al.	Mar 2016	A1
20160093212	Barfield, Jr. et al.	Mar 2016	A1
20160101783	Abou-Nasr et al.	Apr 2016	A1
20160104250	Allen et al.	Apr 2016	A1
20160105365	Droste et al.	Apr 2016	A1
20160116293	Grover et al.	Apr 2016	A1
20160116913	Niles	Apr 2016	A1
20160117871	McClellan et al.	Apr 2016	A1
20160117928	Hodges et al.	Apr 2016	A1
20160125735	Tuukkanen	May 2016	A1
20160129917	Gariepy et al.	May 2016	A1
20160140783	Catt et al.	May 2016	A1
20160140784	Akanuma et al.	May 2016	A1
20160147226	Akselrod et al.	May 2016	A1
20160163217	Harkness	Jun 2016	A1
20160167652	Slusar	Jun 2016	A1
20160171521	Ramirez et al.	Jun 2016	A1
20160187127	Purohit et al.	Jun 2016	A1
20160187368	Modi et al.	Jun 2016	A1
20160189303	Fuchs	Jun 2016	A1
20160189544	Ricci	Jun 2016	A1
20160200326	Cullinane et al.	Jul 2016	A1
20160203560	Parameshwaran	Jul 2016	A1
20160221575	Posch et al.	Aug 2016	A1
20160229376	Abou Mahmoud et al.	Aug 2016	A1
20160231746	Hazelton et al.	Aug 2016	A1
20160248598	Lin et al.	Aug 2016	A1
20160255154	Kim et al.	Sep 2016	A1
20160264132	Paul et al.	Sep 2016	A1
20160272219	Ketfi-Cherif et al.	Sep 2016	A1
20160275790	Kang et al.	Sep 2016	A1
20160277911	Kang et al.	Sep 2016	A1
20160282874	Kurata et al.	Sep 2016	A1
20160288833	Heimberger et al.	Oct 2016	A1
20160291153	Mossau et al.	Oct 2016	A1
20160292679	Kolin et al.	Oct 2016	A1
20160301698	Katara et al.	Oct 2016	A1
20160303969	Akula	Oct 2016	A1
20160304027	Di Censo et al.	Oct 2016	A1
20160304038	Chen et al.	Oct 2016	A1
20160304091	Remes	Oct 2016	A1
20160313132	Larroy	Oct 2016	A1
20160314224	Wei et al.	Oct 2016	A1
20160321674	Lux	Nov 2016	A1
20160323233	Song et al.	Nov 2016	A1
20160327949	Wilson et al.	Nov 2016	A1
20160343249	Gao et al.	Nov 2016	A1
20160347329	Zelman et al.	Dec 2016	A1
20160370194	Colijn et al.	Dec 2016	A1
20170008487	Ur et al.	Jan 2017	A1
20170015263	Makled et al.	Jan 2017	A1
20170017734	Groh et al.	Jan 2017	A1
20170023945	Cavalcanti et al.	Jan 2017	A1
20170024938	Lindsay	Jan 2017	A1
20170036678	Takamatsu	Feb 2017	A1
20170038773	Gordon et al.	Feb 2017	A1
20170067764	Skupin et al.	Mar 2017	A1
20170072967	Fendt et al.	Mar 2017	A1
20170076396	Sudak	Mar 2017	A1
20170076606	Gupta et al.	Mar 2017	A1
20170080900	Huennekens et al.	Mar 2017	A1
20170084175	Sedlik et al.	Mar 2017	A1
20170086028	Hwang et al.	Mar 2017	A1
20170106876	Gordon et al.	Apr 2017	A1
20170116794	Gortsas	Apr 2017	A1
20170120761	Kapadia et al.	May 2017	A1
20170123421	Kentley et al.	May 2017	A1
20170123428	Levinson et al.	May 2017	A1
20170136902	Ricci	May 2017	A1
20170147722	Greenwood	May 2017	A1
20170148102	Franke et al.	May 2017	A1
20170148324	High et al.	May 2017	A1
20170154479	Kim	Jun 2017	A1
20170168493	Miller et al.	Jun 2017	A1
20170169627	Kim et al.	Jun 2017	A1
20170176641	Zhu et al.	Jun 2017	A1
20170192428	Vogt et al.	Jul 2017	A1
20170200367	Mielenz	Jul 2017	A1
20170212511	Paiva et al.	Jul 2017	A1
20170234689	Gibson et al.	Aug 2017	A1
20170236210	Kumar et al.	Aug 2017	A1
20170249844	Perkins et al.	Aug 2017	A1
20170270617	Fernandes et al.	Sep 2017	A1
20170274897	Rink et al.	Sep 2017	A1
20170278312	Minster et al.	Sep 2017	A1
20170308082	Ullrich et al.	Oct 2017	A1
20170309092	Rosenbaum	Oct 2017	A1
20170330448	Moore et al.	Nov 2017	A1
20170352274	Kodama	Dec 2017	A1
20180004223	Baldwin	Jan 2018	A1
20180013831	Dey et al.	Jan 2018	A1
20180046198	Nordbruch et al.	Feb 2018	A1
20180053411	Wieskamp et al.	Feb 2018	A1
20180075538	Konrardy et al.	Mar 2018	A1
20180080995	Heinen	Mar 2018	A1
20180091981	Sharma et al.	Mar 2018	A1
20180099678	Absmeier et al.	Apr 2018	A1
20180194343	Lorenz	Jul 2018	A1
20180218453	Crabtree	Aug 2018	A1
20180231979	Miller et al.	Aug 2018	A1
20180284807	Wood et al.	Oct 2018	A1
20180307250	Harvey	Oct 2018	A1
20180345811	Michels et al.	Dec 2018	A1
20190005464	Harris et al.	Jan 2019	A1
20190005745	Patil et al.	Jan 2019	A1
20190087911	Adams et al.	Mar 2019	A1
20190146491	Hu et al.	May 2019	A1
20190146496	Woodrow et al.	May 2019	A1
20190389378	Adams et al.	Dec 2019	A1

Foreign Referenced Citations (22)

Number	Date	Country
102010001006	Jul 2011	DE
102015208358	Nov 2015	DE
700009	Mar 1996	EP
2269884	Mar 2013	EP
3239686	Nov 2017	EP
2268608	Jan 1994	GB
2488956	Sep 2012	GB
2494727	Mar 2013	GB
2002-259708	Sep 2002	JP
10-2014-0144919	Dec 2014	KR
101515496	May 2015	KR
WO-2005083605	Sep 2005	WO
WO-2010034909	Apr 2010	WO
WO-2010062899	Jun 2010	WO
20141092769	Jun 2014	WO
WO-2014139821	Sep 2014	WO
WO-2014148976	Sep 2014	WO
20161028228	Feb 2016	WO
WO-2016028228	Feb 2016	WO
20161067610	May 2016	WO
WO-2016156236	Oct 2016	WO
20171142931	Aug 2017	WO

Non-Patent Literature Citations (406)

Entry
Althoff et al., “Safety Assessment of Driving Behavior in Multi-Lane Traffic for Autonomous Vehicles”, 2009,IEEE (Year: 2009).
Aguilar et al., “Situation assessment in autonomous systems”, 2012, IEEE (Year: 2012).
Guo et al., “Automated and Safe Vulnerability Assessment”, 2005, IEEE (Year: 2005).
Barltrop et al., “Automated Generation and Assessment of Autonomous Systems Test Cases”, 2008, IEEE (Year: 2008).
Petillo et al., “Autonomous Adaptive Environmental Assessment and Feature Tracking via Autonomous Underwater Vehicles”, 2010 , IEEE (Year: 2010).
Teizer et al., “Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system”, Feb. 2010, Elsevier B.V. (Year: 2010).
“Driverless Cars . . . The Future is Already Here”, AutoInsurance Center, downloaded from the Internet at: <http://www.autoinsurancecenter.com/driverless-cars...the-future-is-already-here.htm> (2010; downloaded on Mar. 27, 2014).
“Integrated Vehicle-Based Safety Systems (IVBSS)”, Research and Innovative Technology Administration (RITA), http://www.its.dot.gov/ivbss/, retrieved from the internet on Nov. 4, 2013, 3 pages.
“Linking Driving Behavior to Automobile Accidents and Insurance Rates: An Analysis of Five Billion Miles Driven”, Progressive Insurance brochure (Jul. 2012).
“Self-Driving Cars: The Next Revolution”, KPMG, Center for Automotive Research (2012).
The Influence of Telematics on Customer Experience: Case Study of Progressive's Snapshot Program, J.D. Power Insights, McGraw Hill Financial (2013).
Advisory Action dated Apr. 1, 2015 for U.S. Appl. No. 14/269,490, 4 pgs.
Alberi et al., A proposed standardized testing procedure for autonomous ground vehicles, Virginia Polytechnic Institute and State University, 63 pages (Apr. 29, 2008).
Birch, ‘Mercedes-Benz’ world class driving simulator complex enhances moose safety, SAE International, Automotive Engineering (Nov. 13, 2010).
Broggi et al., Extensive Tests of Autonomous Driving Technologies, IEEE Trans on Intelligent Transportation Systems, 14(3):1403-15 (May 30, 2013).
Campbell et al., Autonomous Driving in Urban Environments: Approaches, Lessons, and Challenges, Phil. Trans. R. Soc. A, 368:4649-72 (2010).
Carroll et al. “Where Innovation is Sorely Needed”, http://www.technologyreview.com/news/422568/where-innovation-is-sorely-needed/?nlid, retrieved from the internet on Nov. 4, 2013, 3 pages.
Davies, Avoiding Squirrels and Other Things Google's Robot Car Can't Do, downloaded from the Internet at: <http://www.wired.com/2014/05/google-self-driving-car-can-cant/ (downloaded on May 28, 2014).
Davies, Here's How Mercedes-Benz Tests its New Self-Driving Car, Business Insider (Nov. 20, 2012).
Duffy et al., Sit, Stay, Drive: The Future of Autonomous Car Liability, SMU Science & Technology Law Review, vol. 16, pp. 101-123 (Winter 2013).
Figueiredo et al., An Approach to Simulate Autonomous Vehicles in Urban Traffic Scenarios, University of Porto, 7 pages (Nov. 2009).
Franke et al., Autonomous Driving Goes Downtown, IEEE Intelligent Systems, (Nov. 1998).
Funkhouser, Paving the Road Ahead: Autonomous vehicles, products liability, and the need for a new approach, Utah Law Review, vol. 437, Issue 1 (2013).
Garza, “Look Ma, No Hands!” Wrinkles and Wrecks in the Age of Autonomous Vehicles, New England Law Review, vol. 46, pp. 581-616 (2012).
Gechter et al., Towards a Hybrid Real/Virtual Simulation of Autonomous Vehicles for Critical Scenarios, International Academy Research and Industry Association (IARIA), 4 pages (2014).
Gurney, Sue my car not me: Products liability and accidents involving autonomous vehicles, Journal of Law, Technology & Policy (2013).
Hancock et al., “The Impact of Emotions and Predominant Emotion Regulation Technique on Driving Performance,” Work, 41 Suppl 1:5882-5 (Feb. 2012).
Lee et al., Autonomous Vehicle Simulation Project, Int. J. Software Eng. and Its Applications, 7(5):393-402 (2013).
Levendusky, Advancements in automotive technology and their effect on personal auto insurance, downloaded from the Internet at: <http://www.verisk.com/visualize/advancements-in-automotive-technology-and-their-effect> (2013).
Marchant et al., The coming collision between autonomous vehicles and the liability system, Santa Clara Law Review, 52(4): Article 6 (2012).
McCraty et al., “The Effects of Different Types of Music on Mood, Tension, and Mental Clarity.” Alternative Therapies in Health and Medicine 4.1 (1998): 75-84. NCBI PubMed. Web. Jul. 11, 2013.
Mercedes-Benz, Press Information: Networked With All Sense, Mercedes-Benz Driving Simulator (Nov. 2012).
Miller, A simulation and regression testing framework for autonomous workers, Case Western Reserve University, 12 pages (Aug. 2007).
Mui, Will auto insurers survive their collision with driverless cars? (Part 6), downloaded from the Internet at: <http://www.forbes.com/sites/chunkamui/2013/03/28/will-auto-insurers-survive-their-collision> (Mar. 28, 2013).
Office Action in U.S. Appl. No. 14/057,419 dated Mar. 31, 2015.
Office Action in U.S. Appl. No. 14/057,419 dated Oct. 9, 2014.
Office Action in U.S. Appl. No. 14/201,491 dated Apr. 29, 2015.
Office Action in U.S. Appl. No. 14/201,491 dated Jan. 16, 2015.
Office Action in U.S. Appl. No. 14/201,491 dated Sep. 26, 2014.
Office Action in U.S. Appl. No. 14/255,934 dated Jan. 15, 2015.
Office Action in U.S. Appl. No. 14/255,934 dated Jun. 18, 2014.
Office Action in U.S. Appl. No. 14/269,490 dated Jan. 23, 2015.
Office Action in U.S. Appl. No. 14/269,490 dated Jun. 11, 2015.
Office Action in U.S. Appl. No. 14/511,750 dated Dec. 19, 2014.
Office Action in U.S. Appl. No. 14/511,750 dated Jun. 30, 2015.
Office Action in U.S. Appl. No. 14/057,408 dated Jan. 28, 2014.
Office Action in U.S. Appl. No. 14/057,408 dated May 22, 2014.
Office Action in U.S. Appl. No. 14/057,419 dated Jan. 28, 2014.
Office Action in U.S. Appl. No. 14/057,419 dated Jun. 18, 2014.
Office Action in U.S. Appl. No. 14/057,435 dated Jul. 23, 2014.
Office Action in U.S. Appl. No. 14/057,435 dated Mar. 20, 2014.
Office Action in U.S. Appl. No. 14/057,435 dated May 29, 2015.
Office Action in U.S. Appl. No. 14/057,435 dated Nov. 18, 2014.
Office Action in U.S. Appl. No. 14/057,447 dated Aug. 28, 2014.
Office Action in U.S. Appl. No. 14/057,447 dated Dec. 18, 2014.
Office Action in U.S. Appl. No. 14/057,447 dated Feb. 24, 2014.
Office Action in U.S. Appl. No. 14/057,447 dated Jul. 6, 2015.
Office Action in U.S. Appl. No. 14/057,456 dated Mar. 14, 2014.
Office Action in U.S. Appl. No. 14/057,456 dated Oct. 28, 2014.
Office Action in U.S. Appl. No. 14/057,467 dated Feb. 23, 2015.
Office Action in U.S. Appl. No. 14/057,467 dated Jan. 27, 2014.
Office Action in U.S. Appl. No. 14/057,467 dated Jun. 11, 2014.
Office Action in U.S. Appl. No. 14/057,467 dated Oct. 17, 2014.
Office Action in U.S. Appl. No. 14/208,626 dated Apr. 29, 2014.
Office Action in U.S. Appl. No. 14/208,626 dated Aug. 13. 2014.
Office Action in U.S. Appl. No. 14/208,626 dated Dec. 23, 2014.
Office Action in U.S. Appl. No. 14/339,652 dated May 15, 2015.
Office Action in U.S. Appl. No. 14/339,652 dated Oct. 23, 2014.
Office Action in U.S. Appl. No. 14/339,652 dated Sep. 24, 2015.
Office Action in U.S. Appl. No. 14/528,424 dated Feb. 27, 2015.
Office Action in U.S. Appl. No. 14/528,424 dated Jul. 30, 2015.
Office Action in U.S. Appl. No. 14/528,642 dated Jan. 13, 2015.
Office Action in U.S. Appl. No. 14/713,230 dated Oct. 9, 2015.
Office Action in U.S. Appl. No. 14/713,254 dated Oct. 9, 2015.
Office Action in U.S. Appl. No. 14/718,338 dated Jul. 7, 2015.
Office Action, U.S. Appl. No. 14/713,261, dated Oct. 21, 2015.
Pereira, An Integrated Architecture for Autonomous Vehicle Simulation, University of Porto., 114 pages (Jun. 2011).
Peterson, New technology—old law: autonomous vehicles and California's insurance framework, Santa Clara Law Review, 52(4):Article 7 (Dec. 2012).
Pohanka et al., Sensors simulation environment for sensor data fusion, 14th International Conference on Information Fusion, Chicago, IL, pp. 1-8 (2011).
Quinlan et al., Bringing Simulation to Life: A Mixed Reality Autonomous Intersection, Proc. IROS 2010—IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei Taiwan, 6 pages (Oct. 2010).
Read, Autonomous cars & the death of auto insurance, downloaded from the Internet at: <http://www.thecarconnection.com/news/1083266_autonomous-cars-the-death-of-auto-insurance> (Apr. 1, 2013).
Reddy, The New Auto Insurance Ecosystem: Telematics, Mobility and the Connected Car, Cognizant (Aug. 2012).
Reifel et al., “Telematics: The Game Changer—Reinventing Auto Insurance”, A.T. Kearney (2010).
Riley et al., U.S. Appl. No. 14/269,490, filed May 5, 2014.
Roberts, “What is Telematics Insurance?”, MoneySupermarket (Jun. 20, 2012).
Saberi et al., An approach for functional safety improvement of an existing automotive system, IEEE (2015).
Search Report in EP Application No. 13167206.5 dated Aug. 13, 2013, 6 pages.
Sepulcre et al., Cooperative vehicle-to-vehicle active safety testing under challenging conditions, Transportation Research Part C, 26:233-55 (2013).
Sharma, Driving the future: the legal implications of autonomous vehicles conference recap, downloaded from the Internet at: <http://law.scu.edu/hightech/autonomousvehicleconfrecap2012> (Aug. 2012).
Stavens, Learning to Drive: Perception for Autonomous Cars, Stanford University, 104 pages (May 2011).
Stienstra, Autonomous Vehicles & the Insurance Industry, 2013 CAS Annual Meeting—Minneapolis, MN (Nov. 2013).
U.S. Appl. No. 13/844,090, Notice of Allowance, dated Jul. 8, 2014.
U.S. Appl. No. 13/844,090, Office Action, dated Dec. 4, 2013.
U.S. Appl. No. 14/057,408, Notice of Allowance, dated Sep. 25, 2014.
U.S. Appl. No. 14/057,419, Notice of Allowance, dated Oct. 5, 2015.
U.S. Appl. No. 14/057,435, Notice of Allowance, dated Apr. 1, 2016.
U.S. Appl. No. 14/057,447, Final Office Action, dated Jun. 20, 2016.
U.S. Appl. No. 14/057,447, Nonfinal Office Action, dated Dec. 11, 2015.
U.S. Appl. No. 14/057,456, Final Office Action, dated Jun. 16, 2016.
U.S. Appl. No. 14/057,456, Final Office Action, dated Mar. 17, 2015.
U.S. Appl. No. 14/057,456, Nonfinal Office Action, dated Dec. 3, 2015.
U.S. Appl. No. 14/057,467, Final Office Action, dated Mar. 16, 2016.
U.S. Appl. No. 14/057,467, Nonfinal Office Action, dated Jul. 1, 2016.
U.S. Appl. No. 14/057,467, Nonfinal Office Action, dated Nov. 12, 2015.
U.S. Appl. No. 14/201,491, Final Office Action, dated Sep. 11, 2015.
U.S. Appl. No. 14/201,491, Nonfinal Office Action, dated Sep. 26, 2016.
U.S. Appl. No. 14/201,491, Notice of Allowance, dated Apr. 21, 2017.
U.S. Appl. No. 14/208,626, Notice of Allowance, dated May 11, 2015.
U.S. Appl. No. 14/208,626, Notice of Allowance, dated Sep. 1, 2015.
U.S. Appl. No. 14/215,789, filed Mar. 17, 2014, Baker et al., “Split Sensing Method”.
U.S. Appl. No. 14/215,789, Final Office Action, dated Mar. 11, 2016.
U.S. Appl. No. 14/255,934, Final Office Action, dated Sep. 23, 2014.
U.S. Appl. No. 14/255,934, Nonfinal Office Action, dated Jan. 15, 2015.
U.S. Appl. No. 14/255,934, Nonfinal Office Action, dated Jun. 18, 2014.
U.S. Appl. No. 14/255,934, Notice of Allowance, dated May 27, 2015.
U.S. Appl. No. 14/269,490, Final Office Action, dated Jan. 23, 2015.
U.S. Appl. No. 14/269,490, Nonfinal Office Action, dated Sep. 12, 2014.
U.S. Appl. No. 14/339,652, filed Jul. 24, 2014, Freeck et al., “System and Methods for Monitoring a Vehicle Operator and Monitoring an Operating Environment Within the Vehicle”.
U.S. Appl. No. 14/339,652, Final Office Action, dated Apr. 22, 2016.
U.S. Appl. No. 14/339,652, Final Office Action, dated Dec. 13, 2017.
U.S. Appl. No. 14/339,652, Final Office Action, dated Jan. 11, 2017.
U.S. Appl. No. 14/339,652, Nonfinal Office Action, dated Aug. 11, 2016.
U.S. Appl. No. 14/339,652, Nonfinal Office Action, dated Jun. 6, 2017.
U.S. Appl. No. 14/339,652, Nonfinal Office Action, dated Sep. 24, 2015.
U.S. Appl. No. 14/511,712, filed Oct. 10, 2014, Fields et al., “Real-Time Driver Observation and Scoring for Driver's Education”.
U.S. Appl. No. 14/511,712, Final Office Action, dated Jun. 25, 2015.
U.S. Appl. No. 14/511,712, Notice of Allowance, dated Oct. 22, 2015.
U.S. Appl. No. 14/511,712, Office Action, dated Dec. 26, 2014.
U.S. Appl. No. 14/511,750, filed Oct. 10, 2014, Fields et al., Real-Time Driver Observation and Scoring for Driver's Education.
U.S. Appl. No. 14/511,750, Nonfinal Office Action, dated Nov. 3, 2015.
U.S. Appl. No. 14/511,750, Notice of Allowance, dated Mar. 4, 2016.
U.S. Appl. No. 14/528,424, filed Oct. 30, 2014, Christensen et al., “Systems and Methods for Processing Trip-Based Insurance Policies”.
U.S. Appl. No. 14/528,424, Final Office Action, dated Apr. 22, 2016.
U.S. Appl. No. 14/528,424, Final Office Action, dated Feb. 23, 2017.
U.S. Appl. No. 14/528,424, Nonfinal Office Action, dated Dec. 3, 2015.
U.S. Appl. No. 14/528,424, Nonfinal Office Action, dated Sep. 12, 2016.
U.S. Appl. No. 14/528,642, filed Oct. 30, 2014, Christensen et al., “Systems and Methods for Managing Units Associated with Time-Based Insurance Policies”.
U.S. Appl. No. 14/528,642, Final Office Action, dated Jan. 30, 2017.
U.S. Appl. No. 14/528,642, Final Office Action, dated Mar. 9, 2016.
U.S. Appl. No. 14/528,642, Nonfinal Office Action, dated Jul. 5, 2016.
U.S. Appl. No. 14/713,184, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Insurance Pricing”.
U.S. Appl. No. 14/713,184, Final Office Action, dated Jul. 15, 2016.
U.S. Appl. No. 14/713,184, Final Office Action, dated Jun. 29, 2017.
U.S. Appl. No. 14/713,184, Nonfinal office action, dated Mar. 10, 2017.
U.S. Appl. No. 14/713,184, Nonfinal Office Action, dated Feb. 1, 2016.
U.S. Appl. No. 14/713,184,, Notice of Allowance, dated Mar. 20, 2018.
U.S. Appl. No. 14/713,188, Advisory Action, dated Dec. 15, 2017.
U.S. Appl. No. 14/713,188, filed May 15, 2015, Konrardy et al., “Autonomous Feature Use Monitoring and Insurance Pricing”.
U.S. Appl. No. 14/713,188, Final Office Action, dated May 31, 2016.
U.S. Appl. No. 14/713,188 Final Office Action, dated Sep. 8, 2017.
U.S. Appl. No. 14/713,188, Nonfinal Office Action, dated Dec. 3, 2015.
U.S. Appl. No. 14/713,188, Nonfinal Office Action, dated Feb. 24, 2017.
U.S. Appl. No. 14/713,194, filed May 15, 2015, Konrardy et al., “Autonomous Communication Feature Use and Insurance Pricing”.
U.S. Appl. No. 14/713,194, Final Office Action, dated Jan. 25, 2017.
U.S. Appl. No. 14/713,194, Nonfinal Office Action, dated Dec. 28, 2017.
U.S. Appl. No. 14/713,194, Nonfinal Office Action, dated Jul. 29, 2016.
U.S. Appl. No. 14/713,201, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Insurance Pricing and Offering Based Upon Accident Risk Factors”.
U.S. Appl. No. 14/713,201, Final Office Action, dated Sep. 27, 2016.
U.S. Appl. No. 14/713,201, Nonfinal Office Action, dated May 19, 2016.
U.S. Appl. No. 14/713,201, Notice of Allowance, dated Mar. 28, 2017.
U.S. Appl. No. 14/713,206, filed May 15, 2015, Konrardy et al., “Determining Autonomous Vehicle Technology Performance for Insurance Pricing and Offering”.
U.S. Appl. No. 14/713,206, Final Office Action, dated Jun. 29, 2017.
U.S. Appl. No. 14/713,206, Final Office Action, dated May 13, 2016.
U.S. Appl. No. 14/713,206, Nonfinal Office Action, dated Feb. 13, 2017.
U.S. Appl. No. 14/713,206, Nonfinal Office Action, dated Nov. 20, 2015.
U.S. Appl. No. 14/713,214, filed May 15, 2015, Konrardy et al., “Accident Risk Model Determination Using Autonomous Vehicle Operating Data”.
U.S. Appl. No. 14/713,214, Final Office Action, dated Aug. 26, 2016.
U.S. Appl. No. 14/713,214, Nonfinal Office Action, dated Feb. 26, 2016.
U.S. Appl. No. 14/713,214, Notice of Allowance, dated Sep. 11, 2017.
U.S. Appl. No. 14/713,217, Advisory Action, dated Dec. 15, 2017.
U.S. Appl. No. 14/713,217, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Operation Feature Usage Recommendations”.
U.S. Appl. No. 14/713,217, Final Office Action, dated Jul. 22, 2016.
U.S. Appl. No. 14/713,217, Final Office Action, dated Sep. 8, 2017.
U.S. Appl. No. 14/713,217, Nonfinal Office Action, dated Mar. 10, 2017.
U.S. Appl. No. 14/713,217, Nonfinal Office Action, dated Feb. 12, 2016.
U.S. Appl. No. 14/713,223, filed May 15, 2015, Konrardy et al., “Driver Feedback Alerts Based Upon Monitoring Use of Autonomous Vehicle Operation Features”.
U.S. Appl. No. 14/713,223, Final Office Action, dated Sep. 1, 2016.
U.S. Appl. No. 14/713,223, Nonfinal Office Action, dated Feb. 26, 2016.
U.S. Appl. No. 14/713,223, Notice of Allowance, dated May 24, 2017.
U.S. Appl. No. 14/713,226, filed May 15, 2015, Konrardy et al., “Accident Response Using Autonomous Vehicle Monitoring”.
U.S. Appl. No. 14/713,226, Final Office Action, dated May 26, 2016.
U.S. Appl. No. 14/713,226, Nonfinal Office Action, dated Jan. 13, 2016.
U.S. Appl. No. 14/713,226, Notice of Allowance (second), dated Jan. 12, 2017.
U.S. Appl. No. 14/713,226, Notice of Allowance, dated Sep. 22, 2016.
U.S. Appl. No. 14/713,226, Second Notice of Allowance, dated Jan. 12, 2017.
U.S. Appl. No. 14/713,230, filed May 15, 2015, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 14/713,230, Final Office Action, dated Jun. 29, 2017.
U.S. Appl. No. 14/713,230, Final Office Action, dated Mar. 22, 2016.
U.S. Appl. No. 14/713,230, Nonfinal Office Action, dated Feb. 10, 2017.
U.S. Appl. No. 14/713,237, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Technology Effectiveness Determination for Insurance Pricing”.
U.S. Appl. No. 14/713,237, Final Office Action, dated Sep. 9, 2016.
U.S. Appl. No. 14/713,237, Nonfinal Office Action, dated Apr. 18, 2016.
U.S. Appl. No. 14/713,237, Notice of Allowance, dated Aug. 30, 2017.
U.S. Appl. No. 14/713,240, filed May 15, 2015. Konrardy et al., “Fault Determination with Autonomous Feature Use Monitoring”.
U.S. Appl. No. 14/713,240, Final Office Action, dated Sep. 12. 2016.
U.S. Appl. No. 14/713,240, Nonfinal Office Action, dated Apr. 7, 2016.
U.S. Appl. No. 14/713,240, Notice of Allowance, dated Jun. 30, 2017.
U.S. Appl. No. 14/713,244, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Operation Feature Evaluation”.
U.S. Appl. No. 14/713,244, Nonfinal Office Action, dated Dec. 13, 2017.
U.S. Appl. No. 14/713,249, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 14/713,249, Final Office Action, dated Sep. 8, 2017.
U.S. Appl. No. 14/713,249, Final Office Action, dated Jul. 12, 2016.
U.S. Appl. No. 14/713,249, Nonfinal Office Action, dated Mar. 7, 2017.
U.S. Appl. No. 14/713,249, Nonfinal Office Action, dated Jan. 20, 2016.
U.S. Appl. No. 14/713,254, filed May 15, 2015, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 14/713,254, Final Office Action, dated Jun. 29, 2017.
U.S. Appl. No. 14/713,254, Final Office Action, dated Mar. 16, 2016.
U.S. Appl. No. 14/713,254, Nonfinal Office Action, dated Jan. 30, 2017.
U.S. Appl. No. 14/713,261, filed May 15, 2015, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 14/713,261, Final Office Action, dated Apr. 1, 2016.
U.S. Appl. No. 14/713,261, Nonfinal Office Action, dated Feb. 23, 2017.
U.S. Appl. No. 14/713,261, Notice of Allowance, dated Jul. 12, 2017.
U.S. Appl. No. 14/713,266, filed May 15, 2015, Konrardy et al., “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 14/713,266, Final Office Action, dated Sep. 12, 2016.
U.S. Appl. No. 14/713,266, Nonfinal Office Action, dated Mar. 23, 2016.
U.S. Appl. No. 14/713,266, Notice of Allowance, dated May 5, 2017.
U.S. Appl. No. 14/713,271, filed May 15, 2015, Konrardy et al. “Fully Autonomous Vehicle Insurance Pricing”.
U.S. Appl. No. 14/713,271, Final Office Action, dated Jun. 17, 2016.
U.S. Appl. No. 14/713,271, Final Office Action, dated Jun. 29, 2017.
U.S. Appl. No. 14/713,271, Nonfinal Office Action, dated Feb. 28, 2017.
U.S. Appl. No. 14/713,271, Nonfinal Office Action, dated Nov. 6, 2015.
U.S. Appl. No. 14/718,338, Notice of Allowance, dated Nov. 2, 2015.
U.S. Appl. No. 14/729,290, filed Jun. 3, 2015, Fields et al., “Advanced Vehicle Operator Intelligence System”.
U.S. Appl. No. 14/729,290, Notice of Allowance, dated Aug. 5, 2015.
U.S. Appl. No. 14/798,757, Nonfinal Office Action, dated Jan. 17, 2017.
U.S. Appl. No. 14/798,769, Final Office Action, dated Mar. 14, 2017.
U.S. Appl. No. 14/798,769, Nonfinal Office Action, dated Oct. 6, 2016.
U.S. Appl. No. 14/857,242, filed Sep. 17, 2015, Fields et al., “Advanced Vehicle Operator Intelligence System”.
U.S. Appl. No. 14/857,242, Final Office Action, dated Apr. 20, 2016.
U.S. Appl. No. 14/857,242, Nonfinal Office Action, dated Jan. 22, 2016.
U.S. Appl. No. 14/857,242, Notice of Allowance, dated Jul. 1, 2016.
U.S. Appl. No. 14/887,580, Final Office Action, dated Mar. 21, 2017.
U.S. Appl. No. 14/887,580, Nonfinal Office Action, dated Apr. 7, 2016.
U.S. Appl. No. 14/887,580, Nonfinal Office Action, dated Oct. 18, 2016.
U.S. Appl. No. 14/887,580, Nonfinal Office Action, dated Oct. 23, 2017.
U.S. Appl. No. 14/934,326, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Operating Status Assessment”.
U.S. Appl. No. 14/934,326, Nonfinal Office Action, dated Mar. 30, 2018.
U.S. Appl. No. 14/934,333, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Control Assessment and Selection”.
U.S. Appl. No. 14/934,339, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Operator Identification”.
U.S. Appl. No. 14/934,339, Nonfinal Office Action, dated Mar. 14, 2018.
U.S. Appl. No. 14/934,343, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Operating Style and Mode Monitoring”.
U.S. Appl. No. 14/934,343, Nonfinal Office Action, dated Mar. 19, 2018.
U.S. Appl. No. 14/934,345, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Feature Recommendations”.
U.S. Appl. No. 14/934,347, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Software Version Assessment”.
U.S. Appl. No. 14/934,347, Final Office Action, dated Sep. 22, 2017.
U.S. Appl. No. 14/934,347, Nonfinal Office Action, dated Mar. 16, 2017.
U.S. Appl. No. 14/934,347, Notice of Allowance, dated Dec. 15, 2017.
U.S. Appl. No. 14/934,352, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Automatic Parking”.
U.S. Appl. No. 14/934,355, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Insurance Based Upon Usage”.
U.S. Appl. No. 14/934,355, Nonfinal Office Action, dated Mar. 22, 2018.
U.S. Appl. No. 14/934,357, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Salvage and Repair”.
U.S. Appl. No. 14/934,357, Nonfinal Office Action, dated Feb. 28, 2018.
U.S. Appl. No. 14/934,361, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Infrastructure Communication Device”.
U.S. Appl. No. 14/934,361, Final Office Action, dated Jan. 29, 2018.
U.S. Appl. No. 14/934,361, Nonfinal Office Action, dated Jul. 10, 2017.
U.S. Appl. No. 14/934,371, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Accident and Emergency Response”.
U.S. Appl. No. 14/934,371, Final Office Action, dated Oct. 31, 2017.
U.S. Appl. No. 14/934,371, Nonfinal Office Action, dated Jun. 1, 2017.
U.S. Appl. No. 14/934,371, Notice of Allowance, dated Feb. 23, 2018.
U.S. Appl. No. 14/934,381, filed Nov. 6, 2015, Fields et al., “Personal Insurance Policies”.
U.S. Appl. No. 14/934,381, Nonfinal Office Action, dated Feb. 1, 2018.
U.S. Appl. No. 14/934,385, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Operating Status Assessment”.
U.S. Appl. No. 14/934,385, Nonfinal Office Action, dated Apr. 9, 2018.
U.S. Appl. No. 14/934,388, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Control Assessment and Selection”.
U.S. Appl. No. 14/934,388, Nonfinal Office Action, dated Apr. 4, 2018.
U.S. Appl. No. 14/934,393, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Control Assessment and Selection”.
U.S. Appl. No. 14/934,400, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Control Assessment and Selection”.
U.S. Appl. No. 14/934,405, filed Nov. 6, 2015, Fields et al., “Autonomous Vehicle Automatic Parking”.
U.S. Appl. No. 14/934,405, Final Office Action, dated Oct. 31, 2017.
U.S. Appl. No. 14/934,405, Nonfinal Office Action, dated Apr. 20, 2017.
U.S. Appl. No. 14/934,405, Notice of Allowance, dated Jan. 23, 2018.
U.S. Appl. No. 14/950,492, Final Office Action, dated May 3, 2016.
U.S. Appl. No. 14/950,492, Nonfinal Office Action, dated Jan. 22, 2016.
U.S. Appl. No. 14/950,492, Notice of Allowance, dated Aug. 3, 2016.
U.S. Appl. No. 14/951,774, filed Nov. 25, 2015, Konrardy et al., “Fully Autonomous Vehicle Insurance Pricing”.
U.S. Appl. No. 14/951,774, Nonfinal Office Action, dated Feb. 6, 2018.
U.S. Appl. No. 14/951,798, filed Nov. 25, 2015, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 14/951,798, Final Office Action, dated Jul. 26, 2017.
U.S. Appl. No. 14/951,798, Nonfinal Office Action, dated Jan. 27, 2017.
U.S. Appl. No. 14/951,798, Notice of Allowance, dated Feb. 9, 2018.
U.S. Appl. No. 14/951,803, filed Nov. 25, 2015, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 14/951,803, Nonfinal Office Action, dated Feb. 6, 2018.
U.S. Appl. No. 14/978,266, filed Dec. 22, 2015, Konrardy et al., “Autonomous Feature Use Monitoring and Telematics”.
U.S. Appl. No. 14/978,266, Nonfinal Office Action, dated Feb. 7, 2018.
U.S. Appl. No. 15/005,498, Nonfinal Office Action, dated Mar. 31, 2016.
U.S. Appl. No. 15/005,498, Notice of Allowance, dated Aug. 2, 2016.
U.S. Appl. No. 15/076,142, Nonfinal Office Action, dated Aug. 9, 2016.
U.S. Appl. No. 15/076,142, Notice of Allowance, dated Sep. 19, 2016.
U.S. Appl. No. 15/145,993, Nonfinal Office Action, dated May 1, 2017.
U.S. Appl. No. 15/145,993, Notice of Allowance, dated Oct. 25, 2017.
U.S. Appl. No. 15/229,926, filed Aug. 5, 2016, Fields et al., “Advanced Vehicle Operator Intelligence System”.
U.S. Appl. No. 15/229,926, Notice of Allowance, dated Aug. 15, 2017.
U.S. Appl. No. 15/237,832, filed Aug. 16, 2016, Binion et al., “Creating a Virtual Model of a Vehicle Event”.
U.S. Appl. No. 15/241,769, filed Aug. 19, 2016, Fields et al., “Vehicular Traffic Alerts for Avoidance of Abnormal Traffic Conditions”.
U.S. Appl. No. 15/241,769, Nonfinal Office Action, dated Feb. 10, 2017.
U.S. Appl. No. 15/241,769, Notice of Allowance, dated Jul. 7, 2017.
U.S. Appl. No. 15/241,812, filed Aug. 19, 2016, Fields et al., “Using Personal Telematics Data for Rental or Insurance Discounts”.
U.S. Appl. No. 15/241,817, filed Aug. 19, 2016, Fields et al., “Vehicular Accident Risk Monitoring and Assessment”.
U.S. Appl. No. 15/241,826, filed Aug. 19, 2016, Fields et al., “Shared Vehicle Usage, Monitoring and Feedback”.
U.S. Appl. No. 15/241,826, Nonfinal Office Action, dated May 1, 2017.
U.S. Appl. No. 15/241,626, Notice of Allowance, dated Sep. 20, 2017.
U.S. Appl. No. 15/241,832, filed Aug. 19, 2016, Fields et al., “Vehicular Driver Evaluation”.
U.S. Appl. No. 15/241,842, filed Aug. 19, 2016, Fields et al., “Vehicular Driver Warnings”.
U.S. Appl. No. 15/241,849, filed Aug. 19, 2016, Fields et al., “Vehicular Warnings Based Upon Pedestrian or Cyclist Presence”.
U.S. Appl. No. 15/241,849, Nonfinal Office Action, dated Jun. 1, 2017.
U.S. Appl. No. 15/241,849, Notice of Allowance, dated Sep. 29, 2017.
U.S. Appl. No. 15/241,859, filed Aug. 19, 2016, Fields et al., “Determination of Driver or Vehicle Discounts and Risk Profiles Based Upon Vehicular Travel Environment”.
U.S. Appl. No. 15/241,916, filed Aug. 19, 2016, Fields et al., “Determination and Reconstruction of Vehicular Cause and Collision”.
U.S. Appl. No. 15/241,922, filed Aug. 19, 2016, Fields et al., “Electric Vehicle Battery Conservation”.
U.S. Appl. No. 15/241,932, filed Aug. 19, 2016, Fields et al., “Vehicular Driver Profiles and Discounts”.
U.S. Appl. No. 15/255,538, filed Sep. 2, 2016, Fields et al., “Real-Time Driver Observation and Scoring for Driver's Education”.
U.S. Appl. No.15/285,001, filed Oct. 4, 2016, Fields et al., “Real-Time Driver Observation and Scoring for Driver's Education”.
U.S. Appl. No. 15/409,092, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Action Communications”.
U.S. Appl. No. 15/409,099, filed Jan. 18, 2017, Konrardy et al.,“Autonomous Vehicle Path Coordination”.
U.S. Appl. No. 15/409,107, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Signal Control”.
U.S. Appl. No. 15/409,115, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Application”.
U.S. Appl. No. 15/409,115, Nonfinal Office Action, dated Oct. 3, 2017.
U.S. Appl. No. 15/409,115, Notice of Allowance, dated Jan. 26, 2018.
U.S. Appl. No. 15/409,136, filed Jan. 18, 2017, Konrardy et al., “Method and System for Enhancing the Functionality of a Vehicle”.
U.S. Appl. No. 15/409,143, filed Jan. 18, 2017, Konrardy et al., “Autonomous Operation Suitability Assessment and Mapping”.
U.S. Appl. No. 15/409,143, Nonfinal Office Action, dated Jan. 26, 2018.
U.S. Appl. No. 15/409,146, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Routing”.
U.S. Appl. No. 15/409,148, filed Jan. 18, 2017, Konrardy et al., “System and Method for Autonomous Vehicle Sharing Using Facial Recognition”.
U.S. Appl. No. 15/409,149, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Routing During Emergencies”.
U.S. Appl. No. 15/409,159, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Trip Routing”.
U.S. Appl. No. 15/409,163, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Parking”.
U.S. Appl. No. 15/409,167, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Retrieval”.
U.S. Appl. No. 15/409,180, filed Jan. 18, 2017, Konrardy et al., “Method and System for Repairing a Malfunctioning Autonomous Vehicle”.
U.S. Appl. No. 15/409,198, filed Jan. 18, 2017, Konrardy et al., “System and Method for Autonomous Vehicle Ride Sharing Using Facial Recognition”.
U.S. Appl. No. 15/409,213, filed Jan. 18, 2017, Konrardy et al., “Coordinated Autonomous Vehicle Automatic Area Scanning”.
U.S. Appl. No. 15/409,215, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Sensor Malfunction Detection”.
U.S. Appl. No. 15/409,220, filed Jan. 18, 2017, Konrardy et al., “Autonomous Electric Vehicle Charging”.
U.S. Appl. No. 15/409,228, filed Jan. 18, 2017, Konrardy et al., “Operator-Specific Configuration of Autonomous Vehicle Operation”.
U.S. Appl. No. 15/409,236, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Operation Adjustment Based Upon Route”.
U.S. Appl. No. 15/409,239, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Component Maintenance and Repair”.
U.S. Appl. No. 15/409,243, filed Jan. 18, 2017, Konrardy et al., “Anomalous Condition Detection and Response for Autonomous Vehicles”.
U.S. Appl. No. 15/409,248, filed Jan. 18, 2017, Konrardy et al., “Sensor Malfunction Detection”.
U.S. Appl. No. 15/409,271, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Component Malfunction Impact Assessment”.
U.S. Appl. No. 15/409,305, filed Jan. 18, 2017, Konrardy et al., “Component Malfunction Impact Assessment”.
U.S. Appl. No. 15/409,318, filed Jan. 18, 2017, Konrardy et al., “Automatic Repair of Autonomous Vehicles”.
U.S. Appl. No. 15/409,336, filed Jan. 18, 2017, Konrardy et al., “Automatic Repair of Autonomous Components”.
U.S. Appl. No. 15/409,340, filed Jan. 18, 2017, Konrardy et al., “Autonomous Vehicle Damage and Salvage Assessment”.
U.S. Appl. No. 15/409,349, filed Jan. 18, 2017, Konrardy et al., “Component Damage and Salvage Assessment”.
U.S. Appl. No. 15/409,359, filed Jan. 18, 2017, Konrardy et al., “Detecting and Responding to Autonomous Vehicle Collisions”.
U.S. Appl. No. 15/409,371, filed Jan. 18, 2017, Konrardy et al., “Detecting and Responding to Autonomous Environment Incidents”.
U.S. Appl. No. 15/409,445, filed Jan. 18, 2017, Konrardy et al., “Virtual Testing of Autonomous Vehicle Control System”.
U.S. Appl. No. 15/409,473, filed Jan. 18, 2017, Konrardy et al., “Virtual Testing of Autonomous Environment Control System”.
U.S. Appl. No. 15/410,192, filed Jan. 19, 2017, Konrardy et al., “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 15/410,192, Nonfinal Office Action, dated Feb. 25, 2018.
U.S. Appl. No. 15/413,796, filed Jan. 24, 2017, Konrardy et al., “Autonomous Vehicle Refueling”.
U.S. Appl. No. 15/421,508, filed Feb. 1, 2017, Konrardy et al., “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 15/421,508, Nonfinal Office Action, dated Mar. 7, 2018.
U.S. Appl. No. 15/421,521, filed Feb. 1, 2017, Konrardy et al., “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 15/472,813, filed Mar. 29, 2017, Konrardy et al., “Accident Response Using Autonomous Vehicle Monitoring”.
U.S. Appl. No. 15/472,813, Nonfinal Office Action, dated Nov. 22, 2017.
U.S. Appl. No. 15/491,487, filed Apr. 19, 2017, Konrardy et al., “Autonomous Vehicle Insurance Pricing and Offering Based Upon Accident Risk Factors”.
U.S. Appl. No. 15/600,125, filed May 19, 2017, Fields et al., “Vehicle Operator Emotion Management System and Method”.
U.S. Appl. No. 15/600,125, Nonfinal Office Action, dated Jun. 15, 2017.
U.S. Appl. No. 15/600,125, Notice of Allowance, dated Dec. 4, 2017.
U.S. Appl. No. 15/606,049, filed May 26, 2017, Konrardy et al. “Autonomous Vehicle Operation Feature Monitoring and Evaluation of Effectiveness”.
U.S. Appl. No. 15/627,596, filed Jun. 20, 2017, Konrardy et. al., “Driver Feedback Alerts Based Upon Monitoring Use of Autonomous Vehicle Operation Features”.
U.S. Appl. No. 15/676,355, Nonfinal Office Action, dated Nov. 17, 2017.
U.S. Appl. No. 15/689,374, filed Aug. 29, 2017, Konrardy et al., “Fault Determination With Autonomous Feature Use Monitoring”.
U.S. Appl. No. 15/689,437, filed Aug. 29, 2017, Konrardy et al., “Accident Fault Determination for Autonomous Vehicles”.
U.S. Appl. No. 15/806,784, flied Nov. 8, 2017, Konrardy et al., “Accident Risk Model Determination Using Autonomous Vehicle Operating Data”.
U.S. Appl. No. 15/806,789, filed Nov. 8, 2017, Konrardy et al., “Autonomous Vehicle Technology Effectiveness Determination for Insurance Pricing”.
U.S. Appl. No. 15/808,548, Nonfinal Office Action, dated Dec. 14, 2017.
U.S. Appl. No. 15/808,974, filed Nov. 10, 2017, Fields et al., “Vehicular Warnings Based Upon Pedestrian or Cyclist Presence”.
U.S. Appl. No. 15/869,777, Fields et al., “Autonomous Vehicle Software Version Assessment”, filed Jan. 12, 2018.
U.S. Appl. No. 15/895,533, “Autonomous Vehicle Automatic Parking”, filed Feb. 13, 2018.
U.S. Appl. No. 15/907,380, “Accident Fault Determination for Autonomous Vehicles”, filed Feb. 28, 2018.
U.S. Appl. No. 15/935,556, “Autonomous Vehicle Accident and Emergency Response” filed Mar. 26, 2018.
Vasudevan et al., Safe semi-autonomous control with enhanced driver modeling, 2012 American Control Conference, Fairmont Queen Elizabeth, Montreal, Canada (Jun. 27-29, 2012).
Villasenor, Products liability and driverless cars: Issues and guiding principles for legislation, Brookings Center for Technology Innovation, 25 pages (Apr. 2014).
Wang et al., Shader-based sensor simulation for autonomous car testing, 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, Alaska, pp. 224-229 (2012).
Wardzinski, Dynamic risk assessment in autonomous vehicles motion planning, Proceedings of the 2008 1st International Conference on Information Technology, IT 2008, Gdansk, Poland (May 19-21, 2008).
Wiesenthal et al., “The Influence of Music on Driver Stress,” J. Applied Social Psychology, 30(8):1709-19 (Aug. 2000).
Young et al., “Cooperative Collision Warning Based Highway Vehicle Accident Reconstruction”, Eighth International Conference on Intelligent Systems Design and Applications, Nov. 26-28, 2008, pp. 561-565.
Zhou et al., A Simulation Model to Evaluate and Verify Functions of Autonomous Vehicle Based on Simulink, Tongji University, 12 pages (2009).
Saberi et al., “An Approach for Functional Safety Improvement of an Existing Automotive System”, 2015, IEEE (Year: 2015).
Wardzinski, “Dynamic Risk Assessment in Autonomous Vehicles Motion Planning”, May 2008, IEEE (Year: 2008).
Lattner, et al., “Knowledge-based Risk Assessment for Intelligent Vehicles”, 2005, IEEE (Year: 2005).
Vasudevan, “Safe Semi-Autonomous Control with Enhanced Driver Modeling”, Jun. 2012, AACC (Year: 2012).
“A Back-End System for an Autonomous Parking and Charging System for Electric Vehicles” by authors Julian Timpner and Lars Wolf published for the 2012 IEEE International Electric Vehicle Conference and added to IEEE Explore on Apr. 16, 2012.
“NPL—Private Ownership Costs RACQ”, from the Wayback Machine dated Oct. 6, 2014.
“An Evolving Its Paves the Way for Intelligent Highways” by Louis Frenzel, Electronic Design, Jan. 8, 2001.
Al-Shihabi et al., A framework for modeling human-like driving behaviors for autonomous vehicles in driving simulators, Agents'01, pp. 286-291 (2001).
Dittrich et al., Multi-sensor navigation system for an autonomous helicopter, IEEE, pp. 8.C.1-1-8.C.1-9 (2002).
Driverless Cars . . . The Future is Already Here, Autolnsurance Center, downloaded from the Internet at: <http://www.autoinsurancecenter.com/driverless-cars . . . the-future-is-already-here.htm> (2010; downloaded on Mar. 27, 2014).
Filev et al., Future Mobility: Integrating Vehicle Control with Cloud Computing, Mechanical Engineering, 135.3:S18-S24, American Society of Mechanical Engineers (Mar. 2013).
Garza, “Look Ma, No Hands!” Wrinkles and Wrecks in teh Age of Autonomous Vehicles, New England Law Review, vol. 46, pp. 581-616 (2012).
Gerdes et al., Implementable ethics for autonomous vehicles, Chapter 5, IN: Maurer et al. (eds.), Autonomes Fahren, Soringer Vieweg, Berlin (2015).
Gietelink et al., Development of advanced driver assistance systems with vehicle hardware-in-the- loop simulations, Vehicle System Dynamics, vol. 44, No. 7, pp. 569-90 (Jul. 2006).
Gleeson, “How much is a monitored alarm insurance deduction?”, Demand Media (Oct. 30, 2014).
Gray et al., “A Unified Approach to Threat Assessment and Control for Automotive Active Safety”, Sep. 2013, IEEE, vol. 14, No. 3 (Year: 2013).
Hancock et al., “The Impact of Emotions and Predominant Emotion Regulation Technique on Drivina Performance,” Work, 41 Sunni 1:5882-5 (Feb. 2012).
Hars, Autonomous Cars: The Next Revolution Looms, Inventivio GmbH, 4 pages (Jan. 2010).
Lewis, The History of Driverless Cars, downloaded from the Internet at: <www.thefactsite.com/201706/driverless-cars-history.html> (Jun. 2017)
Martin et al., Certification for Autonomous Vehicles, 34 pp., downloaded from the Internet: <https://www.cs.unc.edu/-anderson/teach/comp790a/certification.pdf> (2015).
McCratty et al., “The Effects of Different Types of Music on Mood, Tension, and Mental Clarity.” Alternative Therapies in Health and Medicine 4.1 (1998): 75-84. NCBI PubMed. Web. Jul. 11, 2013.
Private Ownership Costs, RACO, Wayback Machine, http://www.racq.com.au:80/-/media/pdf/racqpdfs/cardsanddriving/cars/0714_vehicle_running_cost s.ashx/ (Oct. 6, 2014).
Ryan, Can having safety features reduce your insurance premiums? (Dec. 15, 2010).
Sharma, Driving the future: the legal implications of autonomous vehicles conference recap, downloaded from the Internet at: <http://law.scu.edu/hightech/autonomousvehicleconfrecap2012> (2012).
Stienstra, Autonomous Vehicles & the Insurance Industry, 2013 CAS Annual Meeting—Minneapolis, MN (2013).
Tiberkak et al., An architecture for policy-based home automation system (Pbhas), 2010 IEEE Green Technologies Conference (Apr. 15-16, 2010).
Wiesenthal, David L., Dwight A. Hennessy, and Brad Totten, “The Influence of Music on Driver Stress,” Journal of Applied Social Psychology 30, 8, pp. 1709-1719, 2000.

Provisional Applications (13)

Number	Date	Country
62103911	Jan 2015	US
62103907	Jan 2015	US
62103895	Jan 2015	US
62103893	Jan 2015	US
62103891	Jan 2015	US
62103856	Jan 2015	US
62103855	Jan 2015	US
62103840	Jan 2015	US
62103838	Jan 2015	US
62103836	Jan 2015	US
62103831	Jan 2015	US
62103914	Jan 2015	US
62079533	Nov 2014	US

Continuations (2)

	Number	Date	Country
Parent	15869777	Jan 2018	US
Child	16406418		US
Parent	14934347	Nov 2015	US
Child	15869777		US

Autonomous vehicle software version assessment

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