Driver identification based on driving maneuver signature

• US 8,989,914 B1
• Filed: 12/19/2011
• Issued: 03/24/2015
• Est. Priority Date: 12/19/2011
• Status: Active Grant

• Current Assignee: Lytx, Inc.
• Original Assignee: Lytx, Inc.
• Inventors: Nemat-Nasser, Syrus C., Smith, Andrew Tombras, Boer, Erwin R.
• Primary Examiner(s): Jabr, Fadey
• Assistant Examiner(s): INGRAM, THOMAS P
• Application Number:
  US13/329,386
• Time in Patent Office: 1,191 Days
• Field of Search: 701/1, 701/409, 701/439, 702/127, 705/7.42, 340/988
• US Class Current: 701/1
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 7/00 Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G07C 5/12 in graphical form
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/16 Anti-collision systems road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08

Abstract

A system for driver identification comprises a processor and a memory. The processor is configured to receive a driving maneuver signature and to determine a driver identification based at least in part on the driving maneuver signature. The memory is coupled to the processor and is configured to provide the processor with instructions.

Claims

1. A system for driver identification, comprising:

a processor configured to;

receive driving data captured during vehicle operation comprising a GPS location data and vehicle sensor data;

identify driving maneuver data at a specific location from the driving data, wherein the specific location is common to a plurality of trips in a database of previously stored driving maneuver signatures associated with known drivers, wherein the GPS location data is used to identify a time interval of the driving maneuver data within the driving data;

determine a driving maneuver signature from the driving maneuver data, wherein the driving maneuver signature includes features and characteristics of vehicle sensor data associated with the specific location and the common driving maneuver; and

determine a similarity of the driving maneuver signature between the plurality of previously stored driving maneuver signatures associated with known drivers at the specific location;

determine a driver identification based on the similarity between the driving maneuver signature to the plurality of previously stored driving maneuver signatures associated with known drivers at the specific location, wherein the driver identification of the driving maneuver signature is identified as the known associated driver of the previously stored driving maneuver signature with a highest similarity; and

a memory coupled to the processor configured to provide the processor with instructions.

2. The system of claim 1, wherein the similarity is determined using a dynamic time warping distance between the driving maneuver signature and one or more of a plurality of previously stored driving maneuver signatures in the database.

3. The system of claim 1, wherein identifying driving maneuver data by using GPS location data is further confirmed by other vehicle sensor data comprising visual images of the specific location.

4. The system of claim 1, wherein the features and characteristics comprising the driving maneuver signature comprise one or more of the following:

a maximum braking level during an approach to a full stop, a maximum cornering level during the right turn maneuver, or a maximum acceleration level, time interval during which acceleration is in a particular range.

5. The system of claim 1, wherein the vehicle sensor data comprises one or more of following:

steering wheel angle, gas pedal position, brake pedal position, absolute velocity, average speed, lateral distance, longitudinal distance, longitudinal velocity, lateral velocity, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw, pitch, or roll.

6. The system of claim 1, wherein the driving maneuver signature comprises one or more of the following driving maneuvers:

a right/left turn maneuver, a highway on/off ramp maneuver, a U-turn maneuver, a lane change maneuver, a vehicle launching from stop maneuver, a vehicle braking maneuver, a curve-handling maneuver, and a car following maneuver.

7. The system of claim 1, wherein the vehicle sensor data further includes environmental temperature and the moisture level.

8. The system of claim 1, wherein the driving maneuver signature further includes data received from one or more external sources that includes at least one of the following:

weather, traffic, and road map information.

9. The system as in claim 1, wherein a trained statistical pattern classifier is used to determine a similarity, wherein the trained statistical pattern classifier estimates the probability that the driving maneuver signature was produced by one of the known drivers.

10. The system as in claim 1, wherein the processor is further configured to build a model of the identified driver based on the determined driving maneuver signature at the specific location.

11. The system as in claim 1, wherein the driving maneuver at the specific location comprises a cornering maneuver that is performed by all drivers as their vehicles exit a fleet yard.

12. A method for driver identification, comprising:

receiving driving data captured during vehicle operation comprising a GPS location data and vehicle sensor data;

identifying driving maneuver data at a specific location from the driving data, wherein the specific location is common to a plurality of trips in a database of previously stored driving maneuver signatures associated with known drivers, wherein the GPS location data is used to identify a time interval of the driving maneuver data within the driving data;

determining a driving maneuver signature from the driving maneuver data, wherein the driving maneuver signature includes features and characteristics of vehicle sensor data associated with the specific location and the common driving maneuver;

determining a similarity of the driving maneuver signature between the plurality of previously stored driving maneuver signatures associated with known drivers at the specific location; and

determining, using a processor, a driver identification based on the similarity between the driving maneuver signature to a plurality of previously stored driving maneuver signatures associated with known drivers at the specific location, wherein the driver identification of the driving maneuver signature is identified as the known associated driver of the previously stored driving maneuver signature with a highest similarity.

13. The method of claim 12, wherein the similarity is determined using a dynamic time warping distance between the driving maneuver signature and one or more of a plurality of previously stored driving maneuver signatures in the database.

14. The method of claim 12, wherein identifying driving maneuver data by using GPS location data is further confirmed by other vehicle sensor data comprising visual images of the specific location.

15. The method of claim 12, wherein the features and characteristics comprising the driving maneuver signature comprise one or more of the following:

a maximum braking level during an approach to a full stop, a maximum cornering level during the right turn maneuver, or a maximum acceleration level, time interval during which acceleration is in a particular range.

16. The method of claim 12, wherein the vehicle sensor data comprises one or more of following:

steering wheel angle, gas pedal position, brake pedal position, absolute velocity, average speed, lateral distance, longitudinal distance, longitudinal velocity, lateral velocity, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw, pitch, or roll.

17. The method of claim 12, wherein the driving maneuver signature comprises one or more of the following driving maneuvers:

a right/left turn maneuver, a highway on/off ramp maneuver, a U-turn maneuver, a lane change maneuver, a vehicle launching from stop maneuver, a vehicle braking maneuver, a curve-handling maneuver, and a car following maneuver.

18. The method of claim 12, wherein the vehicle sensor data further includes environmental temperature and the moisture level.

19. The method of claim 12, wherein the driving maneuver signature further includes data received from one or more external sources that includes at least one of the following:

weather, traffic, and road map information.

20. A computer program product for driver identification, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

receiving driving data captured during vehicle operation comprising a GPS location data and vehicle sensor data;

identifying driving maneuver data at a specific location from the driving data, wherein the specific location is common to a plurality of trips in a database of previously stored driving maneuver signatures associated with known drivers, wherein the GPS location data to is used to identify a time interval of the driving maneuver data within the driving data;

determining a driving maneuver signature from the driving maneuver data, wherein the driving maneuver signature features and characteristics of vehicle sensor data associated with the specific location and the common driving maneuver;

determining a similarity of the driving maneuver signature between the plurality of previously stored driving maneuver signatures associated with known drivers at the specific location; and

determining a driver identification based on the similarity between the driving maneuver signature to of the plurality of previously stored driving maneuver signatures associated with known drivers at the specific location, wherein the driver identification of the driving maneuver signature is identified as the known associated driver of the previously stored driving maneuver signature with a highest similarity.

21. The computer program product of claim 20, wherein the similarity is determined using a dynamic time warping distance between the driving maneuver signature and one or more of a plurality of previously stored driving maneuver signatures in the database.

22. The computer program product of claim 20, wherein identifying driving maneuver data by using GPS location data is further confirmed by other vehicle sensor data comprising visual images of the specific location.

23. The computer program product of claim 20, wherein the features and characteristics comprising the driving maneuver signature comprise one or more of the following:

a maximum braking level during an approach to a full stop, a maximum cornering level during the right turn maneuver, or a maximum acceleration level, time interval during which acceleration is in a particular range.

24. The computer program product of claim 20, wherein the vehicle sensor data comprises one or more of following:

steering wheel angle, gas pedal position, brake pedal position, absolute velocity, average speed, lateral distance, longitudinal distance, longitudinal velocity, lateral velocity, longitudinal acceleration, lateral acceleration, vertical acceleration, yaw, pitch, or roll.

25. The computer program product of claim 20, wherein the driving maneuver signature comprises one or more of the following driving maneuvers:

a right/left turn maneuver, a highway on/off ramp maneuver, a U-turn maneuver, a lane change maneuver, a vehicle launching from stop maneuver, a vehicle braking maneuver, a curve-handling maneuver, and a car following maneuver.

26. The computer program product of claim 20, wherein the vehicle sensor data further includes environmental temperature and the moisture level.

27. The computer program product of claim 20, wherein the driving maneuver signature further includes data received from one or more external sources that includes at least one of the following:

weather, traffic, and road map information.