Patent Application
20220261627
The present disclosure generally relates to vehicles, and more particularly relates to systems and methods for continuously monitoring vehicle in motion and computing comprehensive scores associated with safety and driving quality.
An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. An autonomous vehicle senses its environment using sensing devices such as radar, lidar, image sensors such as cameras, and the like. The autonomous vehicle system further uses information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.
While recent years have seen significant advancements in autonomous vehicle systems, such systems might still be improved in a number of respects. For example, systems and methods that assess driving quality and or safety of the operation of the autonomous vehicle, either by a driver or the vehicle alone, rely on instantaneous and limited information collected from sensors and actuators (e.g., hard brake, sudden acceleration, etc.). This leads to poor and limited estimation of safety and driving quality.
Accordingly, it is desirable to provide systems and methods for continuously monitoring vehicle in motion and computing comprehensive scores associated with safety and driving quality. Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.
Systems and method are provided for monitoring an operator of a vehicle. In one embodiment, a method includes: receiving, by a processor, data generated by the vehicle; determining, by the processor, causal time series event data based on the received data; computing, by the processor, a score for at least one of safety and quality based on a first machine learning model and the causal time series event data; computing, by the processor, at least one explanation for the score based on a second machine learning model; and generating, by the processor, display data to display at least one of the causal time series event data, the score, and the explanation to an end user.
In various embodiments, the first machine model is a deep neural network. In various embodiments, the deep neural network is trained based on ground truth data and crowd sourced driving data.
In various embodiments, the first machine learning model is a gradient boosting machine. In various embodiments, the gradient boosting machine is trained based on ground truth data and crowd sourced driving data.
In various embodiments, the second machine learning model is a classification network that outputs probabilities of score classes and explanations.
In various embodiments, the second machine learning model is a structured causal model that outputs causal explanations.
In various embodiments, the received data comprises sensor data and message data, wherein the determining the causal time series event data comprises processing the sensor data and the message data over a time period to determine a context of a scenario associated with the time period, wherein the causal time series event data includes the context. In various embodiments, the received data comprises actuator data, wherein the determining the causal time series event data comprises processing the actuator data over the time period to determine behavior of actors in the scenario, and wherein the time series event data includes the behavior.
In various embodiments, the causal time series data includes a vector representation of the context concatenated with a vector representation of the behavior.
In another embodiments, system for monitoring an operator of a vehicle includes: a first non-transitory computer module that, by a processor, receives data generated by the vehicle, and determines causal time series event data based on the received data; a second non-transitory module that, by a processor, computes a score for at least one of safety and quality based on a first machine learning model and the causal time series event data; a third non-transitory module that, by a processor, computes at least one explanation for the score based on a second machine learning model; and a fourth non-transitory module that, by a processor, generates display data to display at least one of the causal time series event data, the score, and the explanation to an end user.
In various embodiments, the first machine model is a deep neural network. In various embodiments, the deep neural network is trained based on ground truth data and crowd sourced driving data.
In various embodiments, the first machine learning model is a gradient boosting machine. In various embodiments, the gradient boosting machine is trained based on ground truth data and crowd sourced driving data.
In various embodiments, the second machine learning model is a classification network that outputs probabilities of score classes and explanations.
In various embodiments, the second machine learning model is a structured causal model that outputs causal explanations.
In various embodiments, the received data comprises sensor data and message data, wherein the first non-transitory module determines the causal time series event data by processing the sensor data and the message data over a time period to determine a context of a scenario associated with the time period, wherein the causal time series event data includes the context.
In various embodiments, the received data comprises actuator data, wherein the first non-transitory module determines the causal time series event data by processing the actuator data over the time period to determine behavior of actors in the scenario, and wherein the time series event data includes the behavior.
In various embodiments, the causal time series data includes a vector representation of the context concatenated with a vector representation of the behavior.
The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein:
The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and that the systems described herein is merely exemplary embodiments of the present disclosure.
For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure.
With reference to
In various embodiments, the quality and safety assessing system 100 provides contextual data for use by different stakeholders (e.g., end users, development and safety engineers, insurance providers, etc.). For example, the scores provided by the quality and safety assessing system 100 enable drivers or occupants to receive intuitive, continuous, comprehensive safety scores, and historical data guided safety augmented navigation of the vehicle 10. In another example, the scores provided by quality and safety assessing system 100 enable designers to perform root cause analysis, design fixes, perform regression testing, and deploy system updates. In another example, the scores provided by the quality and safety assessing system 100 enable safety experts to catalogue known safe/unsafe scenarios, discover unknown safe/unsafe scenarios, define safety rules, verify compliance, and generate reports. In still another example, the scores provided by the quality and safety assessing system 100 enable regulators to define federal/state motor vehicle safety standards and certify the autonomous driving systems. In another example, explanations provided by the quality and safety assessing system 100 enable personalized business model development by auto insurance companies using continuous and comprehensive driving quality assessment information.
As depicted in
In various embodiments, the vehicle 10 is an autonomous vehicle and the quality and safety assessing system 100 is incorporated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle 10 is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that any other vehicle including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc., can also be used. In an exemplary embodiment, the autonomous vehicle 10 is autonomous in that it provides partial or full automated assistance to a driver operating the vehicle 10. As used herein the term operator is inclusive of a driver of the vehicle 10 and/or an autonomous driving system of the vehicle 10.
As shown, the autonomous vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a brake system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. The propulsion system 20 may, in various embodiments, include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the vehicle wheels 16-18 according to selectable speed ratios. According to various embodiments, the transmission system 22 may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission. The brake system 26 is configured to provide braking torque to the vehicle wheels 16-18. The brake system 26 may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. The steering system 24 influences a position of the of the vehicle wheels 16-18. While depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system 24 may not include a steering wheel.
The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and/or other sensors.
The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the brake system 26. In various embodiments, the vehicle features can further include interior and/or exterior vehicle features such as, but are not limited to, doors, a trunk, and cabin features such as air, music, lighting, etc. (not numbered).
The communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as but not limited to, other vehicles (“V2V” communication,) infrastructure (“V2I” communication), remote systems, and/or personal devices (described in more detail with regard to
The data storage device 32 stores data for use in automatically controlling the autonomous vehicle 10. In various embodiments, the data storage device 32 stores defined maps of the navigable environment. In various embodiments, the defined maps may be predefined by and obtained from a remote system (described in further detail with regard to
The controller 34 includes at least one processor 44 and a computer readable storage device or media 46. The processor 44 can be any custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller 34, a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer readable storage device or media 46 may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor 44 is powered down. The computer-readable storage device or media 46 may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller 34 in controlling the autonomous vehicle 10.
The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor 44, receive and process signals from the sensor system 28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the autonomous vehicle 10, and generate control signals to the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on the logic, calculations, methods, and/or algorithms. Although only one controller 34 is shown in
In various embodiments, one or more instructions of the controller 34 are embodied in the quality and safety assessing system 100 and, when executed by the processor 44, process sensor data from the sensing devices 40a-40n, message data from the communication medium and/or communication system 36, and/or data sent to or received from the actuator devices 42a-42n, and compute scores and explanations about the safety and driving quality of the operator of the vehicle 10.
With reference now to
The communication network 56 supports communication as needed between devices, systems, and components supported by the operating environment 50 (e.g., via tangible communication links and/or wireless communication links). For example, the communication network 56 can include a wireless carrier system 60 such as a cellular telephone system that includes a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), as well as any other networking components required to connect the wireless carrier system 60 with a land communications system. Each cell tower includes sending and receiving antennas and a base station, with the base stations from different cell towers being connected to the MSC either directly or via intermediary equipment such as a base station controller. The wireless carrier system 60 can implement any suitable communications technology, including for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wireless technologies. Other cell tower/base station/MSC arrangements are possible and could be used with the wireless carrier system 60. For example, the base station and cell tower could be co-located at the same site or they could be remotely located from one another, each base station could be responsible for a single cell tower or a single base station could service various cell towers, or various base stations could be coupled to a single MSC, to name but a few of the possible arrangements.
Apart from including the wireless carrier system 60, a second wireless carrier system in the form of a satellite communication system 64 can be included to provide uni-directional or bi-directional communication with the autonomous vehicles 10a-10n. This can be done using one or more communication satellites (not shown) and an uplink transmitting station (not shown). Uni-directional communication can include, for example, satellite radio services, wherein programming content (news, music, etc.) is received by the transmitting station, packaged for upload, and then sent to the satellite, which broadcasts the programming to subscribers. Bi-directional communication can include, for example, satellite telephony services using the satellite to relay telephone communications between the vehicle 10 and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system 60.
A land communication system 62 may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system 60 to the remote transportation system 52. For example, the land communication system 62 may include a public switched telephone network (PSTN) such as that used to provide hardwired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the land communication system 62 can be implemented through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the remote transportation system 52 need not be connected via the land communication system 62, but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system 60.
Although only one user device 54 is shown in
The remote transportation system 52 includes one or more backend server systems, which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the remote transportation system 52. The remote transportation system 52 can be manned by a live advisor, or an automated advisor, or a combination of both. The remote transportation system 52 can communicate with the user devices 54 and the autonomous vehicles 10a-10n to schedule rides, dispatch autonomous vehicles 10a-10n, and the like. In various embodiments, the remote transportation system 52 stores account information such as subscriber authentication information, vehicle identifiers, profile records, behavioral patterns, and other pertinent subscriber information.
In accordance with a typical use case workflow, a registered user of the remote transportation system 52 can create a ride request via the user device 54. The ride request will typically indicate the passenger's desired pickup location (or current GPS location), the desired destination location (which may identify a predefined vehicle stop and/or a user-specified passenger destination), and a pickup time. The remote transportation system 52 receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles 10a-10n (when and if one is available) to pick up the passenger at the designated pickup location and at the appropriate time. The remote transportation system 52 can also generate and send a suitably configured confirmation message or notification to the user device 54, to let the passenger know that a vehicle is on the way.
As can be appreciated, the subject matter disclosed herein provides certain enhanced features and functionality to what may be considered as a standard or baseline autonomous vehicle 10 and/or an autonomous vehicle based remote transportation system 52. To this end, an autonomous vehicle and autonomous vehicle based remote transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below.
In accordance with various embodiments, the controller 34 implements an autonomous driving system (ADS) 70 to fully or partially operate the vehicle 10 as shown in
In various embodiments, the instructions of the autonomous driving system 70 may be organized by function, module, or system. For example, as shown in
In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and/or path of objects and features of the environment of the vehicle 10. In various embodiments, the computer vision system 74 can incorporate information from the multiple sensors of the sensor system 28, including but not limited to cameras, lidars, radars, and/or any number of other types of sensors.
The positioning system 76 processes sensor data along with other data to determine a position (e.g., a local position relative to a map, an exact position relative to lane of a road, vehicle heading, velocity, etc.) of the vehicle 10 relative to the environment. The guidance system 78 processes sensor data along with other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals for controlling the vehicle 10 according to the determined path.
In various embodiments, the controller 34 implements machine learning techniques to assist the functionality of the controller 34, such as feature detection/classification, obstruction mitigation, route traversal, mapping, sensor integration, ground-truth determination, and the like.
As mentioned briefly above, parts of the quality and safety assessing system 100 of
As can be appreciated, various embodiments of the system 100 according to the present disclosure can include any number of sub-modules and/or datastores. As can be appreciated, the sub-modules and datastores shown in
In various embodiments the causal analyzer module 102 receives sensor data 120, message data 122, and actuator data 124. The sensor data 120 includes data generated by one or more sensing devices of the sensor system 28. The message data 122 includes data generated from messages communicated on the communication medium and/or by the communication system 36. The actuator data 124 includes data generated as a result of controlling one or more actuators of the actuator system 30 (e.g., actuation commands, actuator statuses, etc.).
The causal analyzer module 102 analyzes the received data 120-124 to determine a causal time series of events within a time period and generate time series data 126 based thereon. For example, the causal analyzer module 102 extracts the context of a scenario from the sensor data 120 and the message data 122. The context can include, for example, road features, objects, object types, locations, etc. The causal analyzer module 102 then analyzes the context with the actuator data 124 to determine causes of behavior in the scenario and generates a casual time series of events.
For example, the causal time series can include a listing of actors (e.g., vehicle A, Vehicle B, semi-truck C, traffic signal, etc.), a location of each actor, and an action/status of each actor for time (t) of a scenario (t1-t6). In various embodiments, as shown in
With reference back to
In various embodiments, the score determination module 104 processes the time series data 126 with scenario data indicating known safe/unsafe scenarios and stored in the scenario datastore 114. The score determination module 104 generates and/or updates the safety score based on whether the scenario matches known safe/unsafe scenarios. In various embodiments, when the time series data 126 does not match with a labeled scenario, the time series data 126 is an uncovered unknown scenario and can be sent to an expert for evaluation and labeling of safe/unsafe.
In various embodiments, the score determination module 104 processes the time series data 126 with a baseline safety and behavior model stored in the safe behavior model datastore 112. In various embodiments, the baseline safety and behavior model is a machine learning model implemented as one or more convolutional neural networks, or one or more gradient boosting machines. As can be appreciated, other machine learning models can be implemented, in various embodiments.
In various embodiments, the safety and behavior model provides quantitative values for safety and behavior attributes such as stability, informativeness, cautiousness, attentiveness, etc. These quantitative values are then used to compute an overall quality score. For example, in various embodiments, the overall quality score is computed as:
Where α, β and γ represent a set of weighting parameters for each safety and behavior attributes, respectively, in the machine learning process. ∥·∥ represents an objective function that measures the distance between ground-truth values from the training data and the corresponding predicted values from a machine learning model. Objective functions are minimized in a machine learning process. As can be appreciated, other objective functions as well as combination of those can be implemented, in various embodiments. In various embodiments, the machine learning models are trained based on ground truth data and crowd sourced data from other vehicles.
In various embodiments, the score presentation module 108 receives the score data 128. The score presentation module 108 generates score display data 130 to present the scores in a textual format and/or a graphical format to an end user. For example, as shown in
With reference back to
The explanation presentation module 110 receives the explanation data 132. The explanation presentation module 110 generates explanation display data 134 to present the explanations in a textual and/or graphical format to an end user.
Referring now to
In one example, the method may begin at 205. The sensor data 120, message data 122, and actuator data 124 are received over a time period at 210. The sensor data 120 and the message data 122 are analyzed to determine the context of the scenario in the time period at 220. The actuator data 124 is analyzed with the context data to determine behavior within the time period at 230. The context data and the behavior data are assembled, for example, in vector format to form a time-series causal event chain at 240.
Thereafter, at 250, safety and quality scores are computed by processing the time series causal event chain with a safety and behavior rules and/or models, for example, as discussed above at 260. Thereafter, the causal explanations are determined, for example, based on explanation models as discussed above at 260. The resulting data including the time-series causal event chain, the computed scores, and/or the explanations are presented to the various end users by generating display data at 270. Thereafter, the method may end at 280.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.
