METHOD AND APPARATUS FOR PROVIDING VEHICLE CLASSIFICATION BASED ON AUTOMATION LEVEL

• US 20160247394A1
• Filed: 02/25/2015
• Published: 08/25/2016
• Est. Priority Date: 02/25/2015
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: STENNETH, Leon
• Granted Patent: US 9,728,084 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• 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: 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 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/015 with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles

Abstract

An approach is provided for classifying one or more vehicles based on their level of automation. The approach involves determining training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more automation levels of the one or more vehicles are known. The approach also involves determining one or more sensor signatures for the one or more automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data. The approach further involves causing, at least in part, a classification of one or more other vehicles according to the one or more automation levels based, at least in part, on the one or more sensor signatures and sensor data associated with the one or more other vehicles.

Claims

1. A method comprising:

determining training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more automation levels of the one or more vehicles are known;

determining one or more sensor signatures for the one or more automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data; and

causing, at least in part, a classification of one or more other vehicles according to the one or more automation levels based, at least in part, on the one or more sensor signatures and sensor data associated with the one or more other vehicles.

2. A method of claim 1, further comprising:

determining the one or more signatures, the one or more values of the one or more classification features, or a combination thereof as at least one time series.

3. A method of claim 1, wherein the one or more automation levels include, at least in part, a manually driving vehicle, a partially autonomous vehicle, a fully autonomous vehicle, or a combination thereof.

4. A method of claim 1, wherein the one or more classification features include, at least in part, one or more vehicle status features, one or more driver condition features, one or more environmental features, or a combination thereof.

5. A method of claim 4, wherein the one or more vehicle status features include, at least in part, a relative position of at least one vehicle within at least one lane, a distance between at least one leading vehicle and at least one trailing vehicle relative to at least one target vehicle, an acceleration information for at least one vehicle, or a combination thereof.

6. A method of claim 4, further comprising:

determining the one or more driver condition features based, at least in part, on a limb granularity,

wherein the limb granularity categorizes the one or more driver condition features based, at least in part, one or more features associated with a vehicle operation by foot, a vehicle operation by hand, a vehicle operation by speech, or a combination thereof.

7. A method of claim 6, wherein the one or more features associated with the vehicle operation by foot includes, at least in part, a sensed position and frequency of function of a foot on a brake pedal, a sensed position and frequency of function of a foot on a gas pedal, or a combination thereof;

wherein the one or more features associated with the vehicle operation by hand includes, at least in part, a steering wheel angle, a wiper operation, a blinker operation, a gear shift operation, or a combination thereof; and

wherein the one or more features associated with the vehicle operation by speech includes a use of one or more voice instructions.

8. A method of claim 4, wherein the one or more environmental features include, at least in part, road network information, traffic information, vehicle internal temperature information, external temperature information, weather information, or a combination thereof.

9. A method of claim 4, further comprising:

determining at least one derived feature by combining the one or more vehicle status features, the one or more driver condition features, the one or more environmental features, or a combination thereof as a single feature,

wherein the one or more classification features include, at least in part, the at least one derived feature.

10. A method of claim 1, further comprising:

causing, at least in part, a filtering of the training sensor data, the sensor data associated with the one or more other vehicles based, at least in part, on an outlier suppression.

11. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs,

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following;

determine training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more automation levels of the one or more vehicles are known;

determine one or more sensor signatures for the one or more automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data; and

cause, at least in part, a classification of one or more other vehicles according to the one or more automation levels based, at least in part, on the one or more sensor signatures and sensor data associated with the one or more other vehicles.

12. An apparatus of claim 11, wherein the apparatus is further caused to:

determine the one or more signatures, the one or more values of the one or more classification features, or a combination thereof as at least one time series.

13. An apparatus of claim 11, wherein the one or more automation levels include, at least in part, a manually driving vehicle, a partially autonomous vehicle, a fully autonomous vehicle, or a combination thereof.

14. An apparatus of claim 11, wherein the one or more classification features include, at least in part, one or more vehicle status features, one or more driver condition features, one or more environmental features, or a combination thereof.

15. An apparatus of claim 14, wherein the one or more vehicle status features include, at least in part, a relative position of at least one vehicle within at least one lane, a distance between at least one leading vehicle and at least one trailing vehicle relative to at least one target vehicle, an acceleration information for at least one vehicle, or a combination thereof.

16. An apparatus of claim 14, further comprising:

determine the one or more driver condition features based, at least in part, on a limb granularity,

wherein the limb granularity categorizes the one or more driver condition features based, at least in part, one or more features associated with a vehicle operation by foot, a vehicle operation by hand, a vehicle operation by speech, or a combination thereof.

17. An apparatus of claim 16, wherein the one or more features associated with the vehicle operation by foot includes, at least in part, a sensed position and frequency of function of a foot on a brake pedal, a sensed position and frequency of function of a foot on a gas pedal, or a combination thereof;

wherein the one or more features associated with the vehicle operation by hand includes, at least in part, a steering wheel angle, a wiper operation, a blinker operation, a gear shift operation, or a combination thereof; and

wherein the one or more features associated with the vehicle operation by speech includes a use of one or more voice instructions.

18. A computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

determine training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more automation levels of the one or more vehicles are known;

determine one or more sensor signatures for the one or more automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data; and

cause, at least in part, a classification of one or more other vehicles according to the one or more automation levels based, at least in part, on the one or more sensor signatures and sensor data associated with the one or more other vehicles.

19. A computer-readable storage medium of claim 18, wherein the apparatus is further caused to perform:

determining the one or more signatures, the one or more values of the one or more classification features, or a combination thereof as at least one time series.

20. A computer-readable storage medium of claim 18, wherein the one or more automation levels include, at least in part, a manually driving vehicle, a partially autonomous vehicle, a fully autonomous vehicle, or a combination thereof.

21-48. -48. (canceled)