Method and apparatus for providing vehicle classification based on automation level

• US 9,728,084 B2
• Filed: 02/25/2015
• Issued: 08/08/2017
• 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
• Primary Examiner(s): Do, Truc M
• Application Number:
  US14/631,453
• Publication Number:
  US 20160247394A1
• Time in Patent Office: 895 Days
• Field of Search: None
• 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, by an apparatus, training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more driving automation levels of the one or more vehicles are known;

determining, by the apparatus, one or more sensor signatures for the one or more driving automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data, wherein the one or more classification features include one or more manually driving pattern features and one or more automatically driving pattern features, and the one or more manually driving pattern features include one or more driver physical movement features of manually driving;

determining, by the apparatus, a classification of one or more other vehicles into the one or more driving automation levels based, at least in part, on a comparison of the one or more sensor signatures with sensor data collected during at least one driving operation of the one or more other vehicles and a comparison of the sensor data collected during at least one driving operation of the one or more other vehicles to the one or more manually driving pattern features and the one or more automatically driving pattern features; and

adjusting, by the apparatus, power and bandwidth consumption by sampling additional sensor data from the one or more other vehicles at different frequencies based on the classification.

2. A method of claim 1, further comprising:

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

initiating a presentation on a user interface of the one or more other vehicles based on the classification.

3. A method of claim 1, further comprising:

determining whether the one or more other vehicles are abiding by traffic regulations by applying different traffic rules on the one or more other vehicles based on the classification,

wherein the one or more driving 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 3, wherein the one or more classification features include, at least in part, one or more vehicle status features, one or more environmental features, or a combination thereof, and

wherein the presentation includes one or more visual advertisements when the one or more other vehicles are classified as a fully autonomous vehicle.

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 1, further comprising:

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

wherein the limb granularity categorizes the one or more driver physical movement features based, at least in part, one or more features associated with a vehicle operation by foot, a vehicle operation by hand, 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;

and 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.

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 physical movement 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:

initiating 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.

19. A method of claim 1, wherein the one or more manually driving pattern features and the one or more automatically driving pattern features include one or more relative speeds of the one or more other vehicles from one or more neighboring vehicles, one or more relative distances of the one or more other vehicles from one or more lanes, one or more roadways, the one or more neighboring vehicles, one or more neighboring pedestrians, one or more neighboring traffic lights, one or more neighboring potholes, or a combination thereof.

20. A method of claim 3, further comprising:

initiating, by the apparatus, a transmission of the classification of the one or more other vehicles to a traffic law enforcement entity.

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 driving automation levels of the one or more vehicles are known;

determine one or more sensor signatures for the one or more driving automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data, wherein the one or more classification features include one or more manually driving pattern features and one or more automatically driving pattern features, and the one or more manually driving pattern features include one or more driver physical movement features of manually driving;

determine a classification of one or more other vehicles into the one or more driving automation levels based, at least in part, on a comparison of the one or more sensor signatures with sensor data collected during at least one driving operation of the one or more other vehicles and a comparison of the sensor data collected during at least one driving operation of the one or more other vehicles to the one or more manually driving pattern features and the one or more automatically driving pattern features; and

adjust power and bandwidth consumption by sampling additional sensor data from the one or more other vehicles at different frequencies based on the classification.

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

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

initiate a presentation on a user interface of the one or more other vehicles based on the classification.

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

determine whether the one or more other vehicles are abiding by traffic regulations by applying different traffic rules on the one or more other vehicles based on the classification,

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 physical movement 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. A non-transitory 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:

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

determining one or more sensor signatures for the one or more driving automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data, wherein the one or more classification features include one or more manually driving pattern features and one or more automatically driving pattern features, and the one or more manually driving pattern features include one or more driver physical movement features of manually driving;

determining a classification of one or more other vehicles into the one or more driving automation levels based, at least in part, on a comparison of the one or more sensor signatures with sensor data collected during at least one driving operation of the one or more other vehicles and a comparison of the sensor data collected during at least one driving operation of the one or more other vehicles to the one or more manually driving pattern features and the one or more automatically driving pattern features; and

adjusting power and bandwidth consumption by sampling additional sensor data from the one or more other vehicles at different frequencies based on the classification.

17. A non-transitory computer-readable storage medium of claim 16, 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; and

initiating a presentation on a user interface of the one or more other vehicles based on the classification.

18. A non-transitory computer-readable storage medium of claim 16, wherein the apparatus is further caused to perform:

determining whether the one or more other vehicles are abiding by traffic regulations by applying different traffic rules on the one or more other vehicles based on the classification,

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.