2022 OCT 07 (NewsRx) -- By a News Reporter-Staff News Editor at Technology Business Daily -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor Davis, Timothy Joel (Chicago, IL, US), filed on June 3, 2022, was made available online on September 22, 2022.
No assignee for this patent application has been made.
News editors obtained the following quote from the background information supplied by the inventors: “Automobiles share the roads with many other automobiles. From time to time, these automobiles may be involved in a collision with another automobile or some of other object for various reasons, such as, for example, excess speed, following too closely, or simply a lack of attention.
“At least some new automobiles may include autonomous operation technology that facilitates driver-less operation of the automobile. Such autonomous vehicles may include various sensing technologies that may be used to detect the environment in which the autonomous vehicle operates. The sensing technologies may include, for example, optical sensing, radio frequency sensing, photonic, and acoustic sensing, among others. Such sensing technologies may include proximity sensing technologies that may be used to detect and indicate when the automobile gets near another automobile. Such sensor systems are generally intended to enhance the drivability and safety of the automobile. For example, some automobiles may include forward-looking and rear-looking sensors to assist in parking; side-looking sensors to facilitate blind-spot detection; side-looking sensors for lane detection; and forward looking sensors for navigation and braking systems. Autonomous vehicles may use the sensing technologies to, in some circumstances, avoid a collision among one or more other vehicles, pedestrians, cyclists, road hazards, or immovable objects.
“In the automobile insurance industry, insurance policies are crafted with a variety of considerations in mind, including, the risk a given driver (i.e., the insured) represents to an auto insurance company (i.e., the insurer). A driver represents risk to an auto insurance company in terms of, for example, the likelihood the driver will be involved in a collision. An auto insurance company may consider various other factors in quantifying the risk a given driver represents, including, for example, age, vehicle, occupation, and place of residence. Autonomous vehicles, and their owners, may have substantially different risk profiles when compared to a traditional driver. For example, autonomous vehicles may be more likely to be struck by other vehicles due to the autonomous vehicle’s overly cautious behavior. Such likelihood is further increased in urban traffic. Autonomous vehicles may respond differently to environmental conditions when compared to traditional drivers. In some cases, for example, an autonomous vehicle may lack the ability to safely adapt to unforeseen circumstances, such as downed power lines, flooding, or interference with sensing technology. In other cases, for example, an autonomous vehicle may perform more safely than a traditional driver under certain environmental conditions, such as rain, snow, or loose pavement.
“An insurance company may be more or less likely to offer certain policy features to a driver based upon their risk. For example, an insurance company may be unwilling to offer low-deductible policies to high-risk drivers. Insurance companies often determine policy premiums according to a given driver’s risk. A driver considered a low risk of collision may be offered lower premiums for a collision policy than another driver considered a higher risk of collision. Similarly, a driver who insures an expensive sports car is likely to pay higher premiums for a collision policy than another driver who insures an economy-class, four-door sedan. Likewise, an insurance company may tailor a collision policy to a particular autonomous vehicle based upon its risk profile and driving history.”
As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor’s summary information for this patent application: “The present embodiments may relate to systems and methods for allocating fault to a vehicle involved in a collision, and adjusting auto insurance rates accordingly. Many modern vehicles include various sensors for detecting the environment in which the vehicle is operating before, during, and after the collision. These sensors may include forward-looking sensors, rear-looking sensors, and side-looking sensors that may detect environmental conditions, activity of other automobiles, and activity of pedestrians. This data may be collected and analyzed to determine a fault score for the vehicle representing a percentage of fault for the collision allocated to the vehicle. In certain embodiments, further fault scores may be determined for other vehicles, pedestrians, municipalities, software providers, car makers, and environmental conditions. Fault scores may be relayed to an insurance company for adjusting an auto insurance premium based upon fault scores accumulated over time for an insured automobile, or for another entity with which a given insured automobile interacts.
“In one aspect, a system for allocating fault in a collision involving a vehicle is provided. The system may include (1) a sensor coupled to the vehicle and configured to collect contextual data related to the collision, (2) a non-transitory memory configured to store the contextual data, and (3) a processor coupled to the non-transitory memory and configured to (a) gain access to the contextual data and (b) compute and assign a fault percentage to a driver of the vehicle based upon the contextual data. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.
“In another aspect, a system for allocating fault in a collision involving an autonomous vehicle is provided. The system may include (1) a plurality of sensors coupled to the autonomous vehicle and configured to collect contextual data related to the collision, (2) a first processor coupled to the plurality of sensors and configured to: (a) execute a control program stored in a non-transitory memory to operate the autonomous vehicle, and (b) generate driving data representing operation of the autonomous vehicle by the first processor, and (3) a second processor coupled to the plurality of sensors and the first processor, the second processor configured to: (a) gain access to the contextual data and the driving data, and (b) compute a fault percentage for at least the autonomous vehicle. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.
“In yet another aspect, a method of allocating fault in a collision involving a vehicle is provided. The method may include (1) generating driving data representing operation of the vehicle, (2) detecting contextual information using a plurality of sensors affixed to the vehicle, (3) receiving contextual data representing the contextual information at a processor, and (4) processing, by the processor, the driving data and the contextual data to compute a fault score for the vehicle, the fault score representing a percentage of fault for the collision allocated to an operator of the vehicle. The method may include additional, less, or alternate functionality, including that discussed elsewhere herein, and/or may be implemented, in whole or part, via a computer system, communication network, or one or more local or remote processors, such as those associated with a vehicle, vehicle controller, customer mobile device (e.g., smart phone), and/or insurance provider, and/or via computer-executable instructions stored on non-transitory computer-readable medium or media.
“In another aspect, a premium determination system is provided. The premium determination system may include (1) a communication interface configured to (a) receive contextual data related to a collision involving at least a first vehicle and (b) receive a first fault score and a second fault score for the collision transmitted from the first vehicle, the first fault score representing a first percentage of fault allocated to the first vehicle, the second fault score representing a second percentage of fault allocated to an entity accountable for an environmental condition present at the collision, and (2) a processor coupled to the communication interface and a non-transitory medium, the non-transitory medium containing computer-executable instructions that, when executed by the processor, configure the processor to (a) accumulate respective fault scores for the first vehicle over a period of time, the accumulated fault score for the first vehicle including the first fault score for the collision, and (b) determine an auto insurance premium for the first vehicle based upon the accumulated fault score for the first vehicle. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.
“In yet another aspect, a computer-implemented method of determining auto insurance rates for autonomous vehicles is provided. The computer-implemented method may include (1) receiving contextual data related to a collision involving at least a first vehicle, (2) receiving a first fault score and a second fault score for the collision transmitted from the first vehicle, the first fault score representing a first percentage of fault allocated to the first vehicle, the second fault score representing a second percentage of fault allocated to an entity accountable for an environmental condition present at the collision, (3) accumulating respective fault scores for the first vehicle over a period of time, the accumulated fault score for the first vehicle including the first fault score for the collision, and (4) determining an auto insurance premium for the first vehicle based upon the accumulated fault score for the first vehicle. The method may include additional, less, or alternate functionality, including that discussed elsewhere herein, and/or may be implemented, in whole or part, via a computer system, communication network, or one or more local or remote processors, such as those associated with a vehicle, vehicle controller, customer mobile device (e.g., smart phone), and/or insurance provider, and/or via computer-executable instructions stored on non-transitory computer-readable medium or media.
“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 depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.”
The claims supplied by the inventors are:
“1. An autonomous vehicle system comprising: a computing device remote from a plurality of autonomous vehicle, the computing device comprising a non-transitory memory for storing computer-implementable instructions, and at least one processor, wherein the at least one processor is configured to: receive a plurality of fault scores from a plurality of autonomous vehicle computing devices associated with the plurality of autonomous vehicles, each fault score attributing to each autonomous vehicle a first portion of fault for a collision involving the respective autonomous vehicle; apply machine learning techniques to the plurality of fault scores to identify driving patterns for a set of autonomous vehicles of the plurality of autonomous vehicles being of a similar type; develop a driving profile for the set of autonomous vehicles using the identified driving patterns; receive a first fault score from a first autonomous vehicle computing device associated with a first autonomous vehicle, the first autonomous vehicle being of the similar type to the set of autonomous vehicles; and automatically adjust a first auto insurance premium for the first autonomous vehicle based upon the first fault score and the driving profile for the set of autonomous vehicles.
“2. The autonomous vehicle system of claim 1, wherein the at least one processor is further configured to receive contextual data from a third party system, the contextual data including information collected by one or more sensors of the third party system and associated with one or more collisions involving the first autonomous vehicle.
“3. The autonomous vehicle system of claim 2, wherein the third party system includes a traffic camera system configured to capture images of an area where the one or more collisions occurred.
“4. The autonomous vehicle system of claim 1, wherein the at least one processor is further configured to: accumulate multiple fault scores associated with the first autonomous vehicle during a predetermined time; and apply the machine learning techniques to the accumulated multiple fault score to determine one or more adjustments to the first auto insurance premium for the first autonomous vehicle.
“5. The autonomous vehicle system of claim 1, wherein the at least one processor is further configured to receive a second fault score attributing to at least one other participant at least a second portion of fault for the collision.
“6. The autonomous vehicle system of claim 5, wherein the at least one other participant of the collision includes at least one of a vehicle, a pedestrian, or a municipality.
“7. The autonomous vehicle system of claim 1, wherein the type includes at least one of vehicle make, vehicle model, vehicle year, vehicle software, vehicle software version, or vehicle hardware.
“8. A computer-implemented method implemented using a computing device remote from a plurality of autonomous vehicle, the computing device including a non-transitory memory for storing computer-implementable instructions and at least one processor in communication with the non-transitory memory, said method comprising: receiving a plurality of fault scores from a plurality of autonomous vehicle computing devices associated with the plurality of autonomous vehicles, each fault score attributing to each autonomous vehicle a first portion of fault for a collision involving the respective autonomous vehicle; applying machine learning techniques to the plurality of fault scores to identify driving patterns for a set of autonomous vehicles of the plurality of autonomous vehicles being of a similar type; developing a driving profile for the set of autonomous vehicles using the identified driving patterns; receiving a first fault score from a first autonomous vehicle computing device associated with a first autonomous vehicle, the first autonomous vehicle being of the similar type to the set of autonomous vehicles; and automatically adjusting a first auto insurance premium for the first autonomous vehicle based upon the first fault score and the driving profile for the set of autonomous vehicles.
“9. The method of claim 8 further comprising receiving contextual data from a third party system, the contextual data including information collected by one or more sensors of the third party system and associated with one or more collisions involving the first autonomous vehicle.
“10. The method of claim 9, wherein the third party system includes a traffic camera system configured to capture images of an area where the one or more collisions occurred.
“11. The method of claim 8 further comprising: accumulating multiple fault scores associated with the first autonomous vehicle during a predetermined time; and applying the machine learning techniques to the accumulated multiple fault score to determine one or more adjustments to the first auto insurance premium for the first autonomous vehicle.
“12. The method of claim 8 further comprising receiving a second fault score attributing to at least one other participant at least a second portion of fault for the collision.
“13. The method of claim 12, wherein the at least one other participant of the collision includes at least one of a vehicle, a pedestrian, or a municipality.
“14. The method of claim 8, wherein the type includes at least one of vehicle make, vehicle model, vehicle year, vehicle software, vehicle software version, or vehicle hardware.
“15. At least one non-transitory computer readable medium having computer-executable instructions embodied thereon, when executed by a computing device remote from a plurality of autonomous vehicle and having a non-transitory memory and at least one processor in communication with the non-transitory memory, the computer-executable instructions cause the at least one processor to: receive a plurality of fault scores from a plurality of autonomous vehicle computing devices associated with the plurality of autonomous vehicles, each fault score attributing to each autonomous vehicle a first portion of fault for a collision involving the respective autonomous vehicle; apply machine learning techniques to the plurality of fault scores to identify driving patterns for a set of autonomous vehicles of the plurality of autonomous vehicles being of a similar type; develop a driving profile for the set of autonomous vehicles using the identified driving patterns; receive a first fault score from a first autonomous vehicle computing device associated with a first autonomous vehicle, the first autonomous vehicle being of the similar type to the set of autonomous vehicles; and automatically adjust a first auto insurance premium for the first autonomous vehicle based upon the first fault score and the driving profile for the set of autonomous vehicles.
“16. The computer readable medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to receive contextual data from a third party system, the contextual data including information collected by one or more sensors of the third party system and associated with one or more collisions involving the first autonomous vehicle.
“17. The computer readable medium of claim 16, wherein the third party system includes a traffic camera system configured to capture images of an area where the one or more collisions occurred.
“18. The computer readable medium of claim 15, wherein the computer-executable instructions further cause the at least one processor to: accumulate multiple fault scores associated with the first autonomous vehicle during a predetermined time; and apply the machine learning techniques to the accumulated multiple fault score to determine one or more adjustments to the first auto insurance premium for the first autonomous vehicle.
“19. The computer readable medium of claim 15, wherein the at least one processor is further configured to receive a second fault score attributing to at least one other participant at least a second portion of fault for the collision.
“20. The computer readable medium of claim 15, wherein the type includes at least one of vehicle make, vehicle model, vehicle year, vehicle software, vehicle software version, or vehicle hardware.”
For additional information on this patent application, see: Davis, Timothy Joel. Systems And Methods For Allocating Fault To Autonomous Vehicles. Filed June 3, 2022 and posted September 22, 2022. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220220301069%22.PGNR.&OS=DN/20220301069&RS=DN/20220301069
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