Patent Application
20210110480
The present invention generally relates to electronic systems for vehicular risk assessment. More specifically, various embodiments of the present invention relate to machine sensing and machine learning-based commercial vehicle insurance risk scoring systems. Furthermore, various embodiments of the present invention also relate to autonomous machine-sensing and machine determination of commercial vehicle accident and damage risks for objective and accurate vehicle insurance risk score calculations.
Conventional methods of determining insurance risk for commercial vehicles involve data analysis of past and historical records and statistics. For example, an insurance risk price model make take a company's and/or driver's accident history, safety and accident statistics for particular vehicle models and makes, past traffic tickets issued to commercial drivers, and/or other historical data that have already occurred in the past. In the conventional vehicle insurance modeling, past records are used as primary indicators of the future risk. Unfortunately, in many instances, past accident and macro-statistical records are often outdated, inaccurate, or irrelevant for deriving a precise real-time and realistic assessment of insurance pricing risks associated with a particular commercial vehicle company operating a specific set of vehicle fleets and commercial drivers.
In particular, undesirable distortions in insurance premium pricing modeling for a commercial vehicle insurance often originates from a few statistical outliers within an insured company's commercial drivers. For instance, two “troublemaking” truck drivers out of sixty truck drivers in a commercial trucking company may grossly distort the overall insurance risk assessment modeling typically utilized by a vehicle insurance company, which in turn results in higher risk assessment and thus higher insurance premiums for the entire commercial trucking company. In another example, three “troublemaking” trucks equipped with unreliable brake parts that are prone to at-fault accidents, in contrast to other fifty-seven trucks in the commercial trucking company with good and reliable safety records, may also distort the overall insurance risk assessment modeling, as the vehicle insurance company's conventional insurance premium modeling simply points to a higher overall risk assessment for the entire organization. In this case, the conventional insurance premium modeling based on purely historical data would fail to identify the three troublemaking trucks preemptively with a high level of confidence and granularity to offer a discounted quote to the trucking company, if those three trucks were to be removed from the insurance plan.
Therefore, it may be desirable to devise a novel electronic system configured to incorporate vehicular sensory parameters and electronic commercial driver log and behavioral information in real-time to extrapolate most relevant vehicle insurance risk factors for precise determinations of insurance premiums.
Furthermore, it may also be desirable to devise an intelligent machine-determined commercial vehicle insurance risk scoring module for the novel electronic system that infuses historical accident risk statistics and real-time vehicular sensor and driver-related parameters to generate a dynamic and accurate insurance risk score for a particular commercial vehicle or a particular commercial vehicle driver.
In addition, it may also be desirable to utilize the dynamic and accurate insurance risk score derived from the novel electronic system for rapid identification of troublemaking commercial drivers or commercial vehicles that cause outsized insurance premiums, accident risks, and/or regulatory violations. Moreover, it may also be desirable to provide novel electronic user interfaces from the novel electronic system to commercial vehicle fleet operators or insurance companies to identify, alert, and manage insurance risk scores and potential troublemaking entities for reduced insurance premiums, accident risks, and regulatory violations.
Summary and Abstract summarize some aspects of the present invention. Simplifications or omissions may have been made to avoid obscuring the purpose of the Summary or the Abstract. These simplifications or omissions are not intended to limit the scope of the present invention.
In one embodiment of the invention, an intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system is disclosed. This system comprises: a vehicle on-board diagnostics (OBD) device connected to an engine control unit (ECU), an in-vehicle sensor, or a vehicular control chip in a vehicle to record, diagnose, and generate an engine on or off status, vehicle speed data, acceleration and deceleration data, ambient air temperature data, and diagnostic trouble codes (DTCs) as a raw OBD data stream; a vehicle electronic logging device (ELD) connected to the vehicle OBD device, wherein the vehicle ELD is configured to generate a driver-specific ELD log that contains a currently logged-in driver's on-duty, off-duty, and resting activities associated with the vehicle; an accident-causality historical and statistical database executed on a computer server; an intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module connected to the vehicle OBD device, the vehicle ELD, and the accident-causality historical and statistical database to identify a plurality of insurance risk factors, to assign a numerical value for each insurance risk factor per monitored vehicle, and to determine a weighting ratio per insurance risk factor after analyzing the raw OBD data stream, the driver-specific ELD log, and accident-causality statistics from the accident-causality historical and statistical database, wherein each weighing ratio is directly proportional to a closeness of correlation between an insurance risk factor and an actual accident caused by a particular insurance risk factor; a commercial vehicle insurance risk scoring module connected to the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module, wherein the commercial vehicle insurance risk scoring module derives a commercial vehicle insurance risk score by multiplying the numerical value for each insurance risk factor per monitored vehicle with the weighting ratio per insurance risk factor to generate a plurality of sub-scores from all insurance risk factors, and then by adding all sub-scores and performing a statistical normalization with a min-max feature scaling to produce the commercial vehicle insurance risk score; an ELD and OBD data transceiver connected to the vehicle ELD and the vehicle OBD device, wherein the ELD and OBD data transceiver is configured to transmit the raw OBD data stream and the driver-specific ELD log to components of the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system located outside the vehicle; and a data communication network configured to provide a wireless data information transfer among the ELD and OBD data transceiver, the accident-causality historical and statistical database, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module, and the commercial vehicle insurance risk scoring module.
Specific embodiments of the invention will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.
In the following detailed description of embodiments of the invention, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
The detailed description is presented largely in terms of description of shapes, configurations, and/or other symbolic representations that directly or indirectly resemble one or more novel intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring systems. These descriptions and representations are the means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art.
Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Furthermore, separate or alternative embodiments are not necessarily mutually exclusive of other embodiments. Moreover, the order of blocks in process flowcharts or diagrams representing one or more embodiments of the invention does not inherently indicate any particular order nor imply any limitations in the invention.
For the purpose of describing the invention, a term herein referred to as a “commercial vehicle insurance risk score” is a numerical measure of relative risks to insurance pricing of a commercial vehicle. In a preferred embodiment of the invention, a higher score for one commercial vehicle over a lower score for another commercial vehicle indicates a higher relative risk to the higher-score commercial vehicle, compared to the lower-score commercial vehicle. In some instances, higher commercial insurance risk scores for a group of commercial vehicles may justify imposing higher insurance premiums to the group to account for the higher relative risks for insuring such vehicles. In other instances, higher commercial insurance risk scores may provide an insurance company or a commercial vehicle fleet company a systematic opportunity to remove or reduce vehicles and/or their drivers with insurance risk scores above a predefined threshold value to minimize insurance costs, risks, and/or operational inefficiencies.
In addition, for the purpose of describing the invention, a term herein referred to as a “vehicle on-board diagnostics (OBD) device” is defined as an electronic device installed in a vehicle to collect and/or analyze a variety of vehicle-related data. In one example, the vehicle OBD device outputs many data parameters in real-time, such as vehicle diagnostic information (e.g. engine temperature, oil level, OBD codes, and etc.), fuel consumption-related information, vehicle speed information, vehicle acceleration and deceleration information (i.e. measured in g-force or in SI units), ambient air temperature information, engine rotation-per-minute (RPM) information, vehicle location information, and other vehicle-related data. The OBD device is typically connected to an engine control unit (ECU) and a plurality of in-vehicle control or sensor components, such as an accelerometer, a speedometer, a thermostat, a barometer, an emissions control unit, a vehicle electronics control unit, and any other in-vehicle electronics components to check and diagnose the current condition of each connected vehicle component.
Output data parameters from the vehicle OBD device may be utilized to determine a driver's driving activity status, regulatory compliance on the driver's activities as mandated by municipal, state, or federal authorities, and/or vehicle insurance risks measured by the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system. The output data parameters from the vehicle OBD can also determine a vehicle malfunction status or a vehicle repair need, which can further be utilized by the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system for determining insurance risks, appropriate insurance premiums, and/or removal of “troublemaking” vehicles or drivers who are statistical outliers.
In one example, if the vehicle has a nonzero speed for a certain amount of time while its engine is running, an associated commercial driver's driving activity status may be determined by a vehicle electronic logging device as being engaged in an “on-duty” status. In another example, if the vehicle has a zero speed for a certain amount of time while its engine is idling, the associated commercial driver's driving activity status may be determined by the vehicle electronic logging device as still being engaged in an “on-duty” status. On the other hand, if the vehicle's engine itself is turned off for a certain amount of time, the associated commercial driver's driving activity status may be determined by the vehicle electronic logging device as being “off-duty,” inactive, and/or restful from work. Furthermore, an OBD malfunction code or an abnormal data reading as part of the output data parameters from the vehicle OBD device may indicate or identify the source and the state of the vehicle malfunction.
These data parameters may also be correlated to timestamps generated by an electronic clock associated with the vehicle OBD device. In one embodiment of the invention, the data parameters may be generated by the vehicle OBD device in a region-specific, maker-specific, and/or model-specific format, which requires interpretation and conversion to a compatible output format decodable by a vehicle electronic logging device, a mobile application executed on a portable electronic device, a remotely-located commercial vehicle fleet monitoring station, and/or an intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system.
Furthermore, for the purpose of describing the invention, a term herein referred to as a “vehicle electronic logging device,” or an “ELD,” is defined as a specialized driver activity log-generating electronic device connected to a vehicle OBD device. This specialized driver activity log-generating electronic device analyzes real-time OBD output data parameters to objectively derive or confirm an ongoing driver activity and/or vehicle repair needs in a commercial vehicle. For example, a vehicle ELD can measure and objectively confirm a commercial vehicle driver's on-duty driving by tracking a nonzero vehicle speed data parameter and an engine “on” status signal from the vehicle OBD device, until the commercial vehicle driver stops and turns off the engine.
Similarly, the vehicle ELD can objectively measure and confirm the commercial vehicle driver's off-duty resting period with a system clock and a duration of the engine “off” status signal. These machine and sensor-based autonomous determination of driving behaviors, fatigue driving, and/or accumulated miles driving per day can be further utilized as significant factors in calculating and extrapolating insurance risk scores from the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system.
Moreover, the vehicle ELD may be configured to monitor, track, and record vehicle malfunction codes from the OBD device and incorporate them automatically in a driver vehicle inspection report, which may be initiated, updated, or rectified by a commercial vehicle driver and/or a designated auto mechanic. In addition, regulatory compliance related to a required duration of the commercial vehicle driver's rest can also be tracked and alerted to appropriate authorities (e.g. local, national, and/or federal traffic safety enforcement agencies, fleet managers, etc.) and/or insurance companies by the vehicle ELD and/or other components of the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system.
Furthermore, for the purpose of describing the invention, a term herein referred to as an “hour of service,” or “HoS” is defined as a real-time, hourly, and/or minutely-managed and monitored commercial driving activity parameters and logs for commercial vehicle regulatory compliance required by state, municipal, and/or federal government agencies. For example, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system may incorporate an electronic logging device (ELD) hour-of-service (HoS) audit and correction guidance feature in a vehicle-installed ELD that can provide preemptive regulatory violation (i.e. “pre-violation”) alerts and log amendment capabilities to enable an early-stage correction (i.e. within minutes or hours of a potential pre-violation log element creation) of potentially erroneous commercial driving activity parameters that may have been a result of a driver's carelessness or machine-generated entry errors. Furthermore, the HoS pre-violation or violation alerts may also be utilized as reliable indicators of driver fatigues or driving behavior problems, which are factored into calculation of corresponding insurance risk scores (e.g. increased insurance risk scores for drivers with new pre-violation alerts, etc.) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system.
Moreover, for the purpose of describing the invention, a term herein referred to as a “portable electronic device” is defined as a smart phone, a tablet computer, a notebook computer, a special-purpose proprietary ELD data controller device, or another transportable electronic device that can execute a vehicle ELD HoS audit and correction guidance and management application as well as intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination and scoring modules for a vehicle fleet operator and/or a vehicle insurance company.
Furthermore, for the purpose of describing the invention, a term herein referred to as a “vehicle fleet monitoring station,” or a “remote monitoring station unit” is defined as a vehicle fleet monitoring location for one or more commercial vehicles in operation. Examples of remote monitoring station units include, but are not limited to, a commercial vehicle operation control center, a regulatory traffic safety enforcement agency, a vehicle insurance risk analysis center, a vehicle monitoring service center, and a fleet vehicle employer's information technology (IT) control center. Typically, the remote monitoring station unit is configured to execute and operate an intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system as well as a commercial fleet-level multiple vehicle ELD HoS audit and correction management application in a computer server, a portable electronic device, another computerized device, or a combination thereof.
In addition, for the purpose of describing the invention, a term herein referred to as “computer server” is defined as a physical computer system, another hardware device, a software module executed in an electronic device, or a combination thereof. Furthermore, in one embodiment of the invention, a computer server is connected to one or more data networks, such as a local area network (LAN), a wide area network (WAN), a cellular network, and the Internet. Moreover, a computer server can be utilized by an intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system for gathering and analyzing electronically-generated real-time in-vehicle sensor outputs, accident-causality historical statistics downloaded from government agencies, and real-time commercial vehicle driver electronic logs to determine accurate real-time insurance risk scores for commercial vehicles in operation.
One aspect of an embodiment of the present invention is providing a novel electronic system that incorporates vehicular sensory parameters and electronic commercial driver log and behavioral information in real-time to extrapolate most relevant vehicle insurance risk factors for precise determinations of insurance premiums.
Furthermore, another aspect of an embodiment of the present invention is providing an intelligent machine-determined commercial vehicle insurance risk scoring module for the novel electronic system that infuses historical accident risk statistics and real-time vehicular sensor and driver-related parameters to generate a dynamic and accurate insurance risk score for a particular commercial vehicle or a particular commercial vehicle driver.
In addition, another aspect of an embodiment of the present invention is utilizing the dynamic and accurate insurance risk score derived from the novel electronic system for rapid identification of troublemaking commercial drivers or commercial vehicles that cause outsized insurance premiums, accident risks, and/or regulatory violations.
Yet another aspect of an embodiment of the present invention is providing novel electronic user interfaces from the novel electronic system to commercial vehicle fleet operators or insurance companies to identify, alert, and manage insurance risk scores and potential troublemaking entities for reduced insurance premiums, accident risks, and regulatory violations.
In a preferred embodiment of the invention, the SaaS commercial vehicle and cargo compliance asset tracking operating software platform (111) and various in-vehicle hardware devices and sensors (101, 103, 105, 107, 109) enable rapid and real-time in-vehicle sensor and driver behavior-related data parameter transmissions to the remaining parts (e.g. 115, 121) of the system to conduct machine-sensing and machine-learning (113) to determine dynamic in-vehicle components of commercial vehicle insurance risk factors and derive an intelligent machine sensing and machine learning-based commercial vehicle insurance risk score (123), as shown in
If the commercial vehicle insurance risk score (123) is intended to be utilized by a vehicle insurance company, then the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system may also receive and incorporate the insurance company's preference parameters (117), which may include, for example, data output filter conditions and “worst offending vehicle” or “worst offending driver”-identifying criteria that can realistically reduce risks to a commercial vehicle insurance pricing model for a particular commercial vehicle fleet client. For example, the insurance company may initially request a list of drivers and/or vehicles subject to commercial vehicle insurance risk scores above 90 out of 100 through the insurance company's preference parameters (117) connected to the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system. Then, the insurance company may also require the particular commercial vehicle fleet client to terminate or remove the troublemakers (i.e. “worst offending” drivers and/or related vehicles) scoring above 90 out of 100, if the client wants to continue the insurance coverage at the current insurance premium rates.
Furthermore, in the preferred embodiment of the invention, the in-vehicle sensors and devices (101) integrated into the vehicle include, but are not limited to, an engine control unit (ECU), a vehicle on-board diagnostics device (OBD), a location tracking (e.g. GPS) sensor, a fuel consumption calculator, vehicle maintenance-related sensors, vehicle accelerometers, tire pressure sensors, and any other embedded in-vehicle sensors. Furthermore, the commercial driver activity-tracking device (103) may include an hour-of-service (HoS) commercial driving activity and behavioral analytics device that incorporates government-regulated electronic logging device (ELD) driver log entry and revision capabilities as well as distinctly novel and unique features specific to the HoS analytics device, such as tracking and determining a particular commercial driver's driving behaviors (e.g. speeding, sudden accelerations or decelerations, unsafe cornering), driving fatigue indicators, and granular or subtle real-time driving danger cues (e.g. approaching a dangerous threshold towards a regulatory violation or repeated dangerous driving behaviors within a short timeframe).
For example, the hour-of-service (HoS) commercial driving activity and behavioral analytics system device in the commercial driver activity-tracking device (103) can be configured to provide preemptive regulatory violation (i.e. “pre-violation”) alerts and log amendment capabilities to enable an early-stage correction (i.e. within minutes or hours of a potential pre-violation log element creation) of potentially erroneous commercial driving activity parameters that may have been a result of a driver's carelessness or machine-generated entry errors. Importantly, in context of operating the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system, the HoS pre-violation or violation alerts generated by the hour-of-service (HoS) commercial driving activity and behavioral analytics system device in the commercial driver activity-tracking device (103) may be construed by the intelligent machine as reliable indicators of driver fatigues or driving behavior problems, which are then factored into calculation of corresponding insurance risk scores (e.g. increased insurance risk scores for drivers with new pre-violation alerts, etc.) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system.
Furthermore, the commercial trucking load or asset-tracking device (105) may include a GPS-based location tracking capability for shipment items, a door lock sensor in the cargo area of the vehicle to determine loading or unloading timing of the cargo, and/or a cargo area temperature sensor correlated to timestamps to determine historical and real-time ambient temperatures for the cargo area. In the preferred embodiment of the invention, the commercial trucking load or asset-tracking device (105) is operatively connected to machine learning-based commercial vehicle insurance risk factor determination and scoring modules (115, 121) via the SaaS commercial vehicle and cargo compliance asset tracking operating software platform (111) and at least one of the wireless data modem, the cellular data network, and the satellite communication network that accommodate the real-time machine sensing and learning (113), as shown in
Moreover, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system may also include the dashboard camera (107) configured to capture live video footages or still photographs around the commercial vehicle, which in turn are further analyzed by the intelligent and autonomous machine via image pattern recognition, facial expression interpretations, road sign identifications, and/or other artificial intelligent-based image recognition techniques to deduce and extrapolate useful real-time clues related to fatigue driving, driving behaviors, vehicle conditions, or other accident risk factors considered in calculation of the intelligent machine sensing and machine learning-based commercial vehicle insurance risk score (123). In addition, the commercial driver's log digitalization interface (109) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system is able to convert any paper-based maintenance records, driver logs, hand-written notes, or other non-digital information associated with the commercial vehicle and its drivers into digitized media files that can subsequently be fed into the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (115) for identifications of accident risk factors and subsequent machine-determined derivations of the commercial vehicle insurance risk score (123).
Continuing with the embodiment of the invention as shown in
Furthermore, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (115) also receives macro-statistical information from the accident-causality historical and statistical database (119) and client interface setting information, such as the insurance company preference parameters (117), as shown in the high-level system block diagram (100) in
In the preferred embodiment of the invention, the quantifiable weight ratio-based accident risk factors comprise seven distinct categories: a property factor, a “risk zone” factor, a “time of day” factor, a “fatigue driving” factor, a “miles of day” factor, a vehicle condition factor, and a driving behavior factor, as shown in the high-level system block diagram (100) in
Then, as also shown in the high-level system block diagram (100) in
In the preferred embodiment of the invention, a higher numerical value in the commercial vehicle insurance risk score indicates a proportionally higher accident and safety risk for a particular commercial vehicle. Furthermore, the commercial vehicle insurance risk score may be scaled from 0 to 100, wherein the numerical value of “0” indicates the lowest vehicle insurance risk, while the numerical value of “100” indicates the highest vehicle insurance risk due to a higher likelihood of accidents and/or other safety risks. In some cases, each commercial vehicle insurance risk score may be a result of statistical normalizations with min-max feature scaling to bring all comparable values within a certain scale (e.g. 0˜100), even if certain factors and their respective weight ratios are not utilized in a particular data sample. In another embodiment of the invention, the commercial vehicle insurance risk may not be weighted to a rigid scale (e.g. 0˜100), and may not impose any arbitrary upper maximum values in calculating the commercial vehicle insurance scores.
Furthermore, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system may optionally also include an in-vehicle display unit (207) connected to the vehicle ELD (215) and an in-vehicle intelligent machine sensing and machine learning module for insurance risk factor accumulation (209) executed by the vehicle ELD (215) or by another in-vehicle electronic device per commercial vehicle. Moreover, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system may also optionally incorporate a commercial vehicle insurance company's vehicle insurance pricing and data parameter interface (221) executed by the cloud network-connected computer server, as shown in the hardware-level system block diagram (200) in
The commercial vehicle utilized in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system is typically a truck, a van, a bus, or another commercial operation-registered vehicle for commercial transport of freight and/or passengers that involve state, federal, municipal, and/or corporate regulations to ensure appropriate levels of commercial drivers' mandatory resting periods between vehicle operations and vehicle maintenance for public safety. The electronic commercial driving activity logs and maintenance recordkeeping requirements are typically based on mileage, calendar days, and/or hours of service for each commercial driver. In another embodiment of the invention, the commercial vehicle may be a private vehicle (i.e. not registered as a commercially-operated vehicle), which is shared among a plurality of drivers via car ride-sharing services or passenger transport services.
Furthermore, the vehicle OBD device (213) is a specialized electronic device installed in the commercial vehicle to collect and/or analyze a variety of vehicle-related data, including engine on/off status, engine temperature, OBD fault codes, speed, acceleration, ambient air temperature, ambient air pressure, engine rotation-per-minute (RPM), vehicle location, and other vehicle-related output parameters generated by an engine control unit (ECU), a transmission control module (TCM), an accelerometer, a barometer, a fuel pressure sensor, and other in-vehicle sensors or other electronic components (e.g. interior room thermometers, door lock status tracking device, vehicle location tracking device, dashcams, etc.) connected to the vehicle OBD device (213). In the preferred embodiment of the invention as shown in
In the preferred embodiment of the invention, these output data parameters from the vehicle OBD device (213) are also stored and categorized by the in-vehicle intelligent machine sensing and machine learning module for insurance risk factor accumulation (209), which is executed by the vehicle ELD (215) or by another in-vehicle electronic device per commercial vehicle. Furthermore, the commercial vehicle electronic driver log or a driver vehicle inspection report (DVIR) may additionally indicate that the commercial vehicle requires repairs or maintenance work based on OBD fault codes or other data parameters generated from the vehicle OBD device (213). The vehicle OBD device (213) may also be utilized to determine a driver's driving activity status and vehicle property or condition factors associated with potential accident or safety risks via the vehicle electronic logging device (ELD) (215), which requires each driver in the commercial vehicle that may be time-shared with other drivers or used exclusively by one driver to log in or log off electronically to indicate time periods of specific driver activity.
Continuing with the embodiment of the invention as shown in
In the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system, as illustrated by the hardware-level system block diagram (200) in
In the preferred embodiment of the invention, the vehicle-side system components (219) provide the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor database and determination module (205) a variety of in-vehicle dynamic sensor, driver electronic log, and digitized multimedia parameters for vehicle insurance risk calculations and determinations. Some examples of such dynamic in-vehicle sensory and device readout parameters that can be utilized in formulating commercial vehicle insurance risk factors include, but are not limited to, real-time wireless readouts of vehicle ECU outputs, OBD fault codes, location tracking coordinates, fuel consumption information, vehicle maintenance-related alerts, vehicle accelerometer readout values, tire pressure values, hour-of-service (HoS) commercial driving activity and behavioral information derived from the vehicle ELD (215) and the HoS entry and guidance application for the commercial vehicle driver (203), trucking load or asset-tracking device output values for cargos in the commercial vehicle, and dashboard camera footages that include live video footages or still photographs around the commercial vehicle.
Continuing with the embodiment of the invention as shown in
In the preferred embodiment of the invention as shown in
Furthermore, in the embodiment of the invention as shown in the hardware-level system block diagram (200) in
The macro-statistical information may not have been designed to be derived from particular commercial vehicles and their particular drivers in a particular commercial fleet organization that intends to utilize such macro-statistical data as part of the calculations for the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system. Instead, such macro-statistical data are derived from a large set of general commercial driving populations for macro-level accident statistics analysis, and are merely a part of contributing constituents when the system determines, mathematically weighs, and calculates a commercial vehicle insurance risk score by infusing the dynamic real-time in-vehicle information (e.g. in-vehicle sensor, OBD, and ECU readout values, electronic driver log parameters from a particular commercial vehicle, etc.) with the macro statistical accident-causality data that are not vehicle-specific within the commercial fleet organization.
In addition, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system also incorporates client interface setting information, such as the insurance company preference parameters, through the commercial vehicle insurance company's vehicle insurance pricing and data parameter interface (221) executed by the cloud network-connected computer server, as shown in the hardware-level system block diagram (200) in
In the preferred embodiment of the invention, the quantifiable weight ratio-based accident risk factors comprise seven distinct categories: a property factor, a “risk zone” factor, a “time of day” factor, a “fatigue driving” factor, a “miles of day” factor, a vehicle condition factor, and a driving behavior factor. As illustrated subsequently in
Then, the quantifiable values for each category of accident risk factors are then mathematically weighted, ratioed, and utilized in additional calculations in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring module and management application (225) to derive a novel, commercial vehicle-comparing metric called the “commercial vehicle insurance risk score” for each commercial vehicle subscribed to the SaaS commercial vehicle and cargo compliance asset tracking operating software platform. One commercial vehicle insurance risk score derived from the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring module and management application (225) for one particular commercial vehicle is designed to be quantitatively and objectively comparable against another commercial vehicle insurance risk score for another commercial vehicle, both commercial vehicles of which are typically operated by the same commercial fleet.
In the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) as shown in
For example, the machine-sensed and machine-learned real-time property factor, time of day factor, fatigue driving factor, miles of day factor, vehicle condition factor, and driving behavior factor accumulation module (303) may autonomously machine-interpret a prolonged driver activity without resting periods, frequently jerky acceleration or braking, and/or unusual lane wandering to categorize these values into the “fatigue driving” factor. In another example, if the driver is also speeding excessively or swerving recklessly, the machine-sensed and machine-learned real-time property factor, time of day factor, fatigue driving factor, miles of day factor, vehicle condition factor, and driving behavior factor accumulation module (303) may autonomously machine-interpret such date into the driving behavior factor. Yet in another example, incoming machine sensor readout parameters from the commercial vehicle that indicate timestamps during the vehicle operation may be categorized into the “time of day” factor, while a reduced tire pressure readout from the driver's side front tire may be categorized into the vehicle condition factor and correlated to the related timestamp.
Furthermore, the government or third-party accident statistics download module (305) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) is configured to receive and categorize static macro-data statistical parameters from an accident-causality historical and statistical database originating from a government agency, an insurance institute, or a third-party analytics firm. In one example, the macro-data statistical parameters from the accident-causality historical and statistical database include macro statistical information related to accidents occurring frequently or less frequently in particular geographic locations, particular time of the day, particular roads, particular vehicle types, and accident investigation outcomes. These macro-data statistical parameters are categorized into “risk zone” factor, “time of day” factor, and other accident factors from the government or third-party accident statistics download module (305).
Moreover, the vehicle insurance pricing and risk prioritization parameters (307) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) enable incorporation of insurance pricing and insurance risk prioritization preferences from a commercial vehicle fleet insurer or another client company that utilizes insurance risk modeling. In one example, the commercial vehicle fleet insurer may want to identify the bottom ten percent of most accident-prone vehicles and/or commercial drivers within the commercial vehicle fleet. Then, based on the finalized insurance risk scores and identified risks from the commercial insurance risk scoring system, the commercial vehicle fleet insurer may offer a discount to insurance pricing, if the fleet operator is willing to remove the bottom ten percent of most accident-prone vehicles and/or commercial drivers from a list of insured vehicles and drivers. In another example, the commercial vehicle fleet insurer may want to identify the bottom twenty percent of commercial drivers who exhibit irresponsible levels of driver fatigues, when sensed by in-vehicle accelerometers, location tracking, electronic driver log resting requirement violations, etc. Then, based on the finalized insurance risk scores and identified risks from the commercial insurance risk scoring system, the commercial vehicle fleet insurer may want to flag the identified bottom twenty percent of such commercial drivers as “uninsurable” drivers by the commercial vehicle fleet insurer, even if those drivers were to move to another commercial fleet.
Continuing with the embodiment of the invention as shown in
Importantly, in the preferred embodiment of the invention, the machine-determined proportional weighting is autonomously and dynamically determined periodically or in real time while not necessitating a human operator intervention or manual adjustments, based on the inflow of machine-sensed dynamic in-vehicle sensor readout parameters that stream into the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301). The machine-determined proportional weighting of insurance risk factors are subsequently utilized by a vehicle insurance risk scoring module (e.g. 121 in
Furthermore, for some instances where a human operator intervention or adjustment is desired by the commercial fleet organization or the commercial vehicle insurer, the system adjustment and management user interface (311) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) allows a method for the human operator to manually adjust or intervene in specifying particular weighting proportions on various commercial insurance risk factors. In a typical operating circumstances of the system, however, the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) is configured to be fully autonomous from the human operator in making quantitative and qualitative decisions for insurance risk factor proportional weighting determinations and real-time dynamic changes to such machine-initiated weighting determinations, based on the dynamic changes to the incoming in-vehicle sensor and ELD data readout values.
In addition, the information display and communication management module (313) in the intelligent machine sensing and machine learning-based commercial vehicle insurance risk factor determination module (301) enables the human operator or another client to review the quantitative and/or qualitative output values from the commercial insurance risk factor validation and risk factor proportional weighting determination module (309), as shown in
The commercial insurance risk factor weighting calculation and adjustment module (403) is configured to fetch and/or calculate a quantified value for a particular commercial insurance risk factor associated with a particular commercial vehicle, and then multiply that quantified value of the particular commercial insurance risk factor with the machine-determined weight ratio for that factor. Typically, the machine-determined weight ratio for the particular commercial insurance risk factor is processed and transmitted from the commercial vehicle insurance risk factor determination module (e.g. 301 in
Then, in the preferred embodiment of the invention, each weight-adjusted numerical value for each commercial insurance risk factor category per vehicle can be summed together in the commercial vehicle insurance risk score generator (405) to derive a commercial vehicle insurance risk score per monitored vehicle. In some cases, each commercial vehicle insurance risk score may be a result of statistical normalizations with min-max feature scaling to bring all comparable values within a certain scale (e.g. 0˜100), even if certain factors and their respective weight ratios are not utilized in a particular data sample.
Furthermore, in the preferred embodiment of the invention, a higher numerical value in the commercial vehicle insurance risk score indicates a proportionally higher accident and safety risk for a particular commercial vehicle. For example, the commercial vehicle insurance risk score may be scaled from 0 to 100, wherein the numerical value of “0” indicates the lowest vehicle insurance risk, while the numerical value of “100” indicates the highest vehicle insurance risk due to a higher likelihood of accidents and/or other safety risks. Statistical min-max feature scaling may be utilized to compute the normalized final insurance risk score, which becomes comparable against other scores from other vehicles within the preferred range of scale (e.g. 0˜100).
In another embodiment of the invention, the commercial vehicle insurance risk may not be weighted to a rigid scale (e.g. 0˜100), and may not impose any arbitrary upper maximum values in calculating the commercial vehicle insurance scores. Moreover, a plurality of weight-adjusted numerical values for all risk factor categories may undergo calculations other than above-mentioned summations per weighted factor (e.g. deriving a weighted average of all values or a median value from the plurality of weight-adjusted numerical values instead) in other embodiments of the commercial vehicle insurance risk score generator (405).
Continuing with the embodiment of the invention as shown in
A plurality of commercial vehicle insurance scores for a monitored vehicle fleet from the commercial vehicle insurance risk score generator (405) and machine-determined high risk vehicle information from the high risk vehicle determination and alert module (407) are then transmitted to a commercial vehicle insurance company's vehicle insurance pricing and data parameter interface (413), as shown in
Examples of such critical events include, but are not limited to, vehicles' loss of control, traveling or stationary status of vehicles, other vehicles in the lane resulting in accidents, other vehicles encroaching into the lane resulting in accidents, pedestrians, animals, or objects involved in accidents, and second-derivative accidents (i.e. “vehicle not involved in first harmful event), which can be further categorized by specific geographic zones and times of day. In some embodiments of the invention, these macro accident-causality statistics can also be utilized by the intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system in determining vehicle property, vehicle condition, fatigue driving, and/or driving behavior factors in commercial vehicle insurance risk score derivations.
Furthermore, in the commercial insurance risk factor proportional weighting determination and adjustment example (600) as shown in
In the commercial insurance risk factor proportional weighting determination and adjustment example (600), the vehicle property factor is assigned a six percent weight, while the risk zone factor and the time of day factor are each assigned a twenty-nine percent weight, respectively, for overall calculation of the insurance risk score. Moreover, the fatigue driving factor is assigned a fifteen percent weight and the miles of day factor is assigned a five percent weight, while the vehicle condition factor is assigned a six percent weight and the driving behavior factor is assigned a ten percent weight for the overall calculation of the insurance risk score. In this example, the weighting scale is designed to be out of one hundred percent when all insurance risk factors are combined to produce a single insurance risk metric called the “commercial vehicle insurance risk score,” for each monitored commercial vehicle.
The commercial insurance risk factor proportional weighting determination and adjustment example (600) also illustrates some of the key features (603) of the commercial vehicle insurance risk scoring system as embodied in this invention. In particular, the proportional weighting for each commercial vehicle insurance risk factor is based on real-time machine-determined adjustments from machine-sensing and machine-learning of incoming real-time in-vehicle sensor and electronic driver log readout parameters as well as monitored vehicle-independent accident-causality statistics from a macro-level vehicle accident analytical entity (e.g. a government agency, an insurance institute, a third-party analytical firm, etc.). Furthermore, the proportional weighting is utilized subsequently in calculating a commercial vehicle insurance risk score per monitored vehicle. In addition, an insurance company's or another client's data condition or filter preference can also be incorporated into the system for client-tailored identification of high-risk vehicles, drivers, and insurance risk scores, as shown in
In the preferred embodiment of the invention, the weighting ratio for the “time of day” factor in context of the overall calculation of an insurance risk score is not typically derived from a single vehicle-specific monitoring activity, such as real-time readouts from in-vehicle sensors or electronic driver logs. However, in some embodiments of the invention, sensor or driver log readouts from numerous vehicle-specific monitoring activities may also be utilized in determining the weighting ratio for the “time of day” factor by assigning quantitative significance to timing of past accident occurrences in various accidents in a 24-hour cycle.
In the example (800) as shown in
For example, the government agency may require a mandatory 30 minute break after a consecutive 8-hour drive, and determine compliance by engine on/off status and/or electronic driver log updates. Likewise, the government agency may require an 11-hour driver operations limit even if the driving was non-consecutive, a 14-hour shift limit, or a 60-hour cycle limit per week, as illustrated in
Furthermore, this example (1000) also shows a total number of violations for a selected group of commercial drivers categorized by calendar dates. The SaaS commercial vehicle and cargo compliance asset tracking operating software platform (e.g. 111 in
For instance, higher cumulative violation incidents for the selected commercial drivers result in higher numerical values for fatigue driving and driving behavior factors, before being multiplied by specific weighting ratios for the fatigue driving and driving behavior factors, as illustrated, for example, in
In some instances, an insurance company may want to receive a list of such “worst offenders” in HoS violations to remove them from an insurable pool of commercial drivers. In another instance, the insurance company may want to levy higher insurance premiums for those “worst offenders” or for an vehicle fleet organization containing one or more of those “worst offenders,” as displayed in the example (1100) in
Furthermore, as shown in the example (1200) in
In the preferred embodiment of the invention, if the monitored commercial vehicle has more trouble codes or adverse in-vehicle sensor output values, then the numerical value of the vehicle condition factor component of the insurance risk score increases proportionally. Similarly, if the monitored commercial vehicle has less trouble codes or adverse in-vehicle sensor output values, then the numerical value of the vehicle condition factor component of the insurance risk score decreases proportionally.
In general, if the monitored commercial vehicle generates more nontrivial DTCs over a day, a week, a month, or another predefined measurement period, numerical values for the property and the vehicle condition factor components in insurance risk score calculations increase proportionally. Likewise, if the monitored commercial vehicle generates less number of nontrivial DTCs or no DTCs at all over a predefined measurement period, the numerical values for the property and the vehicle condition factor components in insurance risk score calculations decrease proportionally.
Furthermore, as shown in this example (1400), the commercial vehicle insurance risk scoring system can also generate a driver-specific safety score associated with driving behaviors for the number of miles driven. The driver-specific safety score in
Furthermore, each insurance risk factor has a weighting ratio determined by the commercial vehicle insurance risk factor determination module for computation of insurance risk scores. In this particular example (1500), the weighting ratios are set as 10% for the property factor, 25% for the “risk zone” factor, 25% for the “time of day” factor, 15% for the “fatigue driving” factor, 5% for the “miles of day” factor, 10% for the vehicle condition factor, and 10% for the driving behavior factor. Each weighting ratio per factor is multiplied by a numerical value for each factor, which is typically measured on a scale of 0˜100. Because the “risk zone” factor with the weighting ratio of 25% is not utilized in this particular data sample, each commercial vehicle insurance risk score is a result of statistical normalizations with min-max feature scaling to bring all comparable scores within a certain scale (e.g. 0˜100). The computation of the min-max feature scale is well-known and mathematically well-defined in the field of statistics. In this particular case, the min-max feature scaling is achieved by subtracting the lowest score in the data sample from the score that requires normalization, divided by a result from the lowest score subtracted from the highest score in the data sample, after which the resulting value is multiplied by 100 to complete the min-max feature scaling to 0˜100 range, as exemplified by the data sample results in
For example, for Vehicle #651194 shown as the first entry in
Various embodiments of the present invention provide several key advantages over conventional methods of vehicle insurance risk determinations and related insurance premium pricing. One advantage of an embodiment of the present invention is providing a novel electronic system that incorporates vehicular sensory parameters and electronic commercial driver log and behavioral information in real-time to extrapolate most relevant vehicle insurance risk factors for precise determinations of insurance premiums.
Furthermore, another advantage of an embodiment of the present invention is providing an intelligent machine-determined commercial vehicle insurance risk scoring module for the novel electronic system that infuses historical accident risk statistics and real-time vehicular sensor and driver-related parameters to generate a dynamic and accurate insurance risk score for a particular commercial vehicle or a particular commercial vehicle driver.
In addition, another advantage of an embodiment of the present invention is providing a dynamic and accurate insurance risk score derived from the novel electronic system for rapid identification of troublemaking commercial drivers or commercial vehicles that cause outsized insurance premiums, accident risks, and/or regulatory violations.
Moreover, another advantage of an embodiment of the present invention is providing novel electronic user interfaces from the novel electronic system to commercial vehicle fleet operators or insurance companies to identify, alert, and manage insurance risk scores and potential troublemaking entities for reduced insurance premiums, accident risks, and regulatory violations.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as disclosed herein. Accordingly, the scope of the invention should be limited only by the attached claims.
