Autonomous vehicle action planning using behavior prediction

• US 9,511,767 B1
• Filed: 07/01/2015
• Issued: 12/06/2016
• Est. Priority Date: 07/01/2015
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: Nishi, Tomoki, Okumura, Bunyo, James, Michael R., Sakai, Katsuhiro
• Primary Examiner(s): To, Tuan C.
• Application Number:
  US14/789,004
• Time in Patent Office: 524 Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18154 Approaching an intersection
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle

Abstract

This application describes an automated driving system and methods. The automated driving system includes a perception system disposed on an autonomous vehicle. The automated driving system can detect, using the perception system, information for an environment proximate to the autonomous vehicle. The information for the environment includes current behavior of an object of interest and traffic density information. The automated driving system can also determine a classifier for the environment based on a prediction of future behavior of the object of interest. Based on the classifier, the automated driving system can identify a transition between vehicle states, the vehicle states being associated with a planned action for the autonomous vehicle. The automated driving system can also send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.

Claims

1. An automated driving system, comprising:

a perception system disposed on an autonomous vehicle; and

a computing device in communication with the perception system, comprising;

one or more processors for controlling operations of the computing device; and

a memory for storing data and program instructions used by the one or more processors, wherein the one or more processors are configured to execute instructions stored in the memory to;

detect, using the perception system, information for an environment proximate to the autonomous vehicle, the information including current behavior of an object of interest and traffic density information;

determine a classifier for the environment based on a prediction of future behavior of the object of interest;

based on the classifier, identify a transition between vehicle states, the vehicle states associated with a planned action for the autonomous vehicle; and

send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.

2. The system of claim 1, wherein the object of interest is one of a neighboring vehicle, a pedestrian, and an obstacle.

3. The system of claim 1, wherein the current behavior of the object of interest includes at least one of position and orientation and velocity and acceleration.

4. The system of claim 1, wherein determining the classifier based on the prediction of the future behavior of the object of interest includes comparing the current behavior of the object of interest to at least one of map information and the traffic density information.

5. The system of claim 4, wherein comparing the current behavior of the object of interest to at least one of the map information and the traffic density information includes determining a planned action for the object of interest based on at least one of the map information and the traffic density information, determining a probability for the planned action, and comparing the probability to a predetermined threshold.

6. The system of claim 1, wherein determining the classifier is further based on comparing the current behavior of the object of interest to at least one of training data and current behavior of the autonomous vehicle.

7. The system of claim 1, wherein the planned action for the autonomous vehicle is entering a roundabout and the vehicle states include approach, enter, wait, and merge.

8. The system of claim 1, wherein the planned action for the autonomous vehicle is changing lanes and the vehicle states include gap find, drift, and merge.

9. A computer-implemented method of automated driving, comprising:

detecting, using a perception system disposed on an autonomous vehicle, information for an environment proximate to the autonomous vehicle, the information including current behavior of an object of interest and traffic density information;

determining a classifier for the environment based on a prediction of future behavior of the object of interest;

based on the classifier, identifying a transition between vehicle states, the vehicle states associated with a planned action for the autonomous vehicle; and

sending a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.

10. The method of claim 9, wherein the object of interest is one of a neighboring vehicle, a pedestrian, and an obstacle.

11. The method of claim 9, wherein the current behavior of the object of interest includes at least one of position and orientation and velocity and acceleration.

12. The method of claim 9, wherein determining the classifier based on the prediction of the future behavior of the object of interest includes comparing the current behavior of the object of interest to at least one of map information and the traffic density information.

13. The method of claim 12, wherein comparing the current behavior of the object of interest to at least one of the map information and the traffic density information includes determining a planned action for the object of interest based on at least one of the map information and the traffic density information, determining a probability for the planned action, and comparing the probability to a predetermined threshold.

14. The method of claim 9, wherein determining the classifier is further based on comparing the current behavior of the object of interest to at least one of training data and current behavior of the autonomous vehicle.

15. A computing device, comprising:

one or more processors for controlling operations of the computing device; and

a memory for storing data and program instructions used by the one or more processors, wherein the one or more processors are configured to execute instructions stored in the memory to;

detect, using a perception system disposed on an autonomous vehicle, information for an environment proximate to the autonomous vehicle, the information including current behavior of an object of interest and traffic density information;

determine a classifier for the environment based on the current behavior of the object of interest and a prediction of future behavior of the object of interest;

based on the classifier, identify a transition between vehicle states, the vehicle states associated with a planned action for the autonomous vehicle; and

send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.

16. The computing device of claim 15, wherein the object of interest is one of a neighboring vehicle, a pedestrian, and an obstacle and wherein the current behavior of the object of interest includes at least one of position and orientation and velocity and acceleration.

17. The computing device of claim 15, wherein determining the classifier based on the prediction of the future behavior of the object of interest includes comparing the current behavior of the object of interest to at least one of map information and the traffic density information.

18. The computing device of claim 15, wherein comparing the current behavior of the object of interest to at least one of the map information and the traffic density information includes determining a planned action for the object of interest based on at least one of the map information and the traffic density information, determining a probability for the planned action, and comparing the probability to a predetermined threshold.

19. The computing device of claim 15, wherein determining the classifier based on the current behavior of the object of interest includes comparing the current behavior of the object of interest to at least one of training data and current behavior of the autonomous vehicle.

20. The computing device of claim 15, wherein the planned action for the autonomous vehicle is entering a roundabout and the vehicle states include approach, enter, wait, and merge.