Network computer system for analyzing driving actions of drivers on road segments of a geographic region
First Claim
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1. A method for identifying fraudulent vehicle driving data, the method being performed by one or more processors and comprising:
- receiving, over one or more networks, vehicle data from multiple computing devices, each computing device associated with a corresponding vehicle of multiple vehicles that individually traverse a given road segment of a geographic region, the vehicle data received from each computing device including sensor-based vehicle state information for the vehicle the computing device is associated with and data indicative of one or more attributes of that vehicle'"'"'s motion while the vehicle is traversing the given road segment, the data indicative of one or more attributes of that vehicle'"'"'s motion including Global Positioning System (GPS) data;
for each of the multiple vehicles, determining, based on the vehicle data of that vehicle, a characterization of at least one driving action performed using that vehicle at the given road segment and a confidence value associated with each characterization, wherein each confidence value indicates a reliability level of the associated characterization;
aggregating the characterizations and confidence values determined for the multiple vehicles;
determining, for the given road segment, a baseline or model characterization for the at least one driving action based at least in part on the aggregated characterizations and confidence values;
receiving new vehicle data from an additional vehicle, the new vehicle data including GPS data indicating that the additional vehicle traversed the given road segment and data indicative of one or more attributes of the additional vehicle'"'"'s motion while the additional vehicle is traversing the given road segment;
determining, based on the new vehicle data of the additional vehicle, a characterization of at least one driving action performed using the additional vehicle at the given road segment;
identifying, based on the characterization of the driving action performed using the additional vehicle and the baseline or model characterization for the at least one driving action, that the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data, wherein the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data if the characterization of the driving action performed using the additional vehicle differs from the baseline or model characterization for the at least one driving action by more than a threshold amount;
determining, based on the identified anomalous vehicle data, that the received new vehicle data is spoofed;
identifying a driver of the additional vehicle; and
evaluating the driver based on the determination that the new vehicle data is spoofed.
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Abstract
Examples include a network computer system and/or service which operates to remotely monitor vehicles to detect and characterize driving actions of drivers with respect to specific road segments of a roadway, enabling driving actions performed by multiple drivers at a specific road segment to be characterized and modeled. Models can inform municipalities about potential traffic hazards or other safety challenges. Individual drivers can also be measured against the model to help understand driver performance. Validating vehicle data against baseline values can also detect spoofing of that data.
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Citations
21 Claims
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1. A method for identifying fraudulent vehicle driving data, the method being performed by one or more processors and comprising:
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receiving, over one or more networks, vehicle data from multiple computing devices, each computing device associated with a corresponding vehicle of multiple vehicles that individually traverse a given road segment of a geographic region, the vehicle data received from each computing device including sensor-based vehicle state information for the vehicle the computing device is associated with and data indicative of one or more attributes of that vehicle'"'"'s motion while the vehicle is traversing the given road segment, the data indicative of one or more attributes of that vehicle'"'"'s motion including Global Positioning System (GPS) data; for each of the multiple vehicles, determining, based on the vehicle data of that vehicle, a characterization of at least one driving action performed using that vehicle at the given road segment and a confidence value associated with each characterization, wherein each confidence value indicates a reliability level of the associated characterization; aggregating the characterizations and confidence values determined for the multiple vehicles; determining, for the given road segment, a baseline or model characterization for the at least one driving action based at least in part on the aggregated characterizations and confidence values; receiving new vehicle data from an additional vehicle, the new vehicle data including GPS data indicating that the additional vehicle traversed the given road segment and data indicative of one or more attributes of the additional vehicle'"'"'s motion while the additional vehicle is traversing the given road segment; determining, based on the new vehicle data of the additional vehicle, a characterization of at least one driving action performed using the additional vehicle at the given road segment; identifying, based on the characterization of the driving action performed using the additional vehicle and the baseline or model characterization for the at least one driving action, that the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data, wherein the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data if the characterization of the driving action performed using the additional vehicle differs from the baseline or model characterization for the at least one driving action by more than a threshold amount; determining, based on the identified anomalous vehicle data, that the received new vehicle data is spoofed; identifying a driver of the additional vehicle; and evaluating the driver based on the determination that the new vehicle data is spoofed. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer system comprising:
- a network computer system; and
a plurality of service applications which individually execute on a computing device that is carried within a vehicle, each service application of the plurality of service applications being under programmatic control of the network computer system to (i) interface and obtain vehicle data from one or more components on the computing device or accessible to the computing device within the vehicle, and (ii) transmit the vehicle data to the network computer system over one or more networks; wherein the network computer system includes one or more processors and a memory to store instructions, the instructions causing the one or more processors to; receive, over one or more networks, vehicle data from multiple computing devices, each computing device associated with a corresponding vehicle of multiple vehicles that individually traverse a given road segment of a geographic region, the vehicle data received from each computing device being indicative of one or more attributes of the motion of the vehicle the computing device is associated with while the vehicle is traversing the given road segment, the vehicle data including Global Positioning System (GPS) data; for each of the multiple vehicles, determine, based on the vehicle data of that vehicle, a characterization of at least one driving action performed using that vehicle at the given road segment and a confidence value associated with each characterization, wherein each confidence value indicates a reliability level of the associated characterization; aggregate the characterizations and confidence values determined for the multiple vehicles; determine, for the given road segment, a baseline or model characterization for the at least one driving action based at least in part on the aggregated characterizations and confidence values; receive new vehicle data from an additional vehicle, the new vehicle data including GPS data indicating that the additional vehicle traversed the given road segment and data indicative of one or more attributes of the additional vehicle'"'"'s motion while the additional vehicle is traversing the given road segment; determine, based on the new vehicle data of the additional vehicle, a characterization of at least one driving action performed using the additional vehicle at the given road segment; identify, based on the characterization of the driving action performed using the additional vehicle and the baseline or model characterization for the at least one driving action, that the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data, wherein the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data if the characterization of the driving action performed using the additional vehicle differs from the baseline or model characterization for the at least one driving action by more than a threshold amount; determine, based on the identified anomalous vehicle data, that the received new vehicle data is spoofed; identifying a driver of the additional vehicle; and evaluating the driver based on the determination that the new vehicle data is spoofed.
- a network computer system; and
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21. A non-transitory computer-readable medium that stores instructions, which when executed by one or more processors, cause a computing system of the one or more processors to perform operations that include:
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receiving, over one or more networks, vehicle data from multiple computing devices, each computing device associated with a corresponding vehicle of multiple vehicles that individually traverse a given road segment of a geographic region, the vehicle data received from each computing device being indicative of one or more attributes of the motion of the vehicle the computing device is associated with while the vehicle is traversing the given road segment, the vehicle data including Global Positioning System (GPS) data; for each of the multiple vehicles, determining, based on the vehicle data of that vehicle, a characterization of at least one driving action performed using that vehicle at the given road segment and a confidence value associated with each characterization, wherein each confidence value indicates a reliability level of the associated characterization; aggregate the characterizations and confidence values determined for the multiple vehicles; determine, for the given road segment, a baseline or model characterization for the at least one driving action based at least in part on the aggregated characterizations and confidence values; receive new vehicle data from an additional vehicle, the new vehicle data including GPS data indicating that the additional vehicle traversed the given road segment and data indicative of one or more attributes of the additional vehicle'"'"'s motion while the additional vehicle is traversing the given road segment; determine, based on the new vehicle data of the additional vehicle, a characterization of at least one driving action performed using the additional vehicle at the given road segment; identify, based on the characterization of the driving action performed using the additional vehicle and the baseline or model characterization for the at least one driving action, that the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data, wherein the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data if the characterization of the driving action performed using the additional vehicle differs from the baseline or model characterization for the at least one driving action by more than a threshold amount; determine, based on the identified anomalous vehicle data, that the received new vehicle data is spoofed; identifying a driver of the additional vehicle; and evaluating the driver based on the determination that the new vehicle data is spoofed.
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Specification