Network computer system for analyzing driving actions of drivers on road segments of a geographic region

• US 10,297,148 B2
• Filed: 12/16/2016
• Issued: 05/21/2019
• Est. Priority Date: 02/17/2016
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Sumers, Theodore Russell
• Primary Examiner(s): Lin, Abby Y
• Application Number:
  US15/382,453
• Publication Number:
  US 20170236411A1
• Time in Patent Office: 886 Days
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2520/125 Lateral acceleration
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• CPC Class Codes: G06F 3/016 Input arrangements with force or tactile feedback as computer generated output to the user
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

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.

Claims

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.

2. The method of claim 1, wherein each computing device is located within or attached to the corresponding vehicle.

3. The method of claim 1, wherein the characterization of at least one driving action performed using the additional vehicle includes a numerical categorization.

4. The method of claim 1, wherein the baseline or model is based on a statistical distribution.

5. The method of claim 1, further comprising:

communicating information corresponding to the baseline or model characterization to a group of drivers within the geographic region.

6. The method of claim 1, further comprising:

determining, from the baseline or model, one or more road segments of the geographic region which include a road hazard.

7. The method of claim 5, further comprising:

obtaining contextual information in conjunction with vehicle data that is received from each of the multiple vehicles; and

wherein determining the baseline or model characterization includes determining the baseline or model characterization based at least in part on a context identified from the contextual information.

8. The method of claim 7, wherein the identified context includes one or more of:

time of day, day of week, month of year, weather condition, temporary road condition, or a public event in the geographic region.

9. The method of claim 8, wherein the baseline characterization is specific to the identified context.

10. The method of claim 1, wherein the vehicle data includes vehicle state information determined from a motion measuring component that resides within each of the vehicles.

11. The method of claim 10, wherein the motion measuring component of at least one of the multiple vehicles includes at least one of an accelerometer or inertial measurement unit (“

IMU”

) that is provided within a mobile computing device of a driver of that vehicle.

12. The method of claim 1, wherein receiving the vehicle data includes receiving multiple types of vehicle state information from the multiple vehicles, each type of vehicle state information corresponding to one of Global Positioning System (“

GPS”

) data, accelerometer data, gyroscopic data, or inertial measurement unit (“

IMU”

) data.

13. The method of claim 12, wherein determining the characterization of at least one driving action includes determining a magnitude of an acceleration action performed by the driver.

14. The method of claim 13, wherein the acceleration action corresponds to a braking action or a steering action of the driver.

15. The method of claim 13, wherein the acceleration action corresponds to a pedal action of the driver.

16. The method of claim 1, further comprising:

evaluating a driving performance of a driver of each of the multiple vehicles based at least in part on a comparison of the characterization of the driving action that is determined for the vehicle of that driver and the baseline of the characterization.

17. The method of claim 1, wherein determining the characterization of the at least one driving action includes:

(a) communicating with a mobile computing device of a driver of each of the multiple vehicles to obtain vehicle state information;

(b) determining a characterization of each of multiple driving actions performed by the driver of each of the multiple vehicles;

(c) correlating at least one of the multiple driving actions performed by the driver of each of the multiple vehicles to one of a plurality of road segments for the geographic region.

18. The method of claim 1, wherein determining, from 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, comprises determining an associated confidence value indicating a reliability level of the associated characterization, and wherein identifying that the characterization of the driving action performed using the additional vehicle represents anomalous vehicle data, is further based on the associated confidence value.

19. The method of claim 1 wherein determining the baseline or model comprises assigning a weight to each of the aggregated characterizations based at least in part on the associated confidence value, the contribution of a particular characterization to the baseline or model being based on the weight assigned to the particular characterization.

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.

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:

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.