Modifying Behavior of Autonomous Vehicle Based on Predicted Behavior of Other Vehicles

• US 20140142799A1
• Filed: 01/28/2014
• Published: 05/22/2014
• Est. Priority Date: 03/15/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ferguson, David Ian Franklin, Dolgov, Dmitri A.
• Granted Patent: US 9,330,571 B2
• Time in Patent Office: Days
• US Class Current: 701/23
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2556/20 Data confidence level
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2756/10 Involving external transmission of data to or from the vehicle
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 60/0011 involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/00274 considering possible movement changes
• CPC Class Codes: B60W 60/00276 for two or more other traffic participants
• CPC Class Codes: G05D 1/00 Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots drive control systems specially adapted for autonomous road vehicles B60W60/00
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 2201/0213 Road vehicle, e.g. car or truck
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

A vehicle configured to operate in an autonomous mode could determine a current state of the vehicle and the current state of the environment of the vehicle. The environment of the vehicle includes at least one other vehicle. A predicted behavior of the at least one other vehicle could be determined based on the current state of the vehicle and the current state of the environment of the vehicle. A confidence level could also be determined based on the predicted behavior, the current state of the vehicle, and the current state of the environment of the vehicle. In some embodiments, the confidence level may be related to the likelihood of the at least one other vehicle to perform the predicted behavior. The vehicle in the autonomous mode could be controlled based on the predicted behavior, the confidence level, and the current state of the vehicle and its environment.

Claims

1. A method, comprising:

receiving, at a computing system configured to control a first vehicle, vehicle information indicative of lane of travel, speed, and pose of the first vehicle, wherein the pose of the first vehicle includes heading and orientation of the first vehicle;

estimating one or more possible future behaviors of a second vehicle in a vicinity of the first vehicle based on location, speed, and pose of the second vehicle relative to the first vehicle and other objects in the vicinity of the first vehicle;

determining respective likelihoods of occurrence for the one or more possible future behaviors of the second vehicle; and

based on the respective likelihoods, determining whether to maintain or modify one or more of the speed, pose, and lane of travel of the first vehicle.

2. The method of claim 1, wherein estimating the one or more possible future behaviors comprises:

estimating the one or more future behaviors further based on road geometry and road condition of a road of travel of the first vehicle.

3. The method of claim 1, wherein estimating the one or more possible future behaviors comprises:

estimating the one or more possible future behaviors further based on a predicted behavior of the second vehicle in response to an action of the first vehicle.

4. The method of claim 1, further comprising:

receiving behavior change information indicating a change in behavior of one or more of the first vehicle, the second vehicle, and the other objects; and

updating the respective likelihoods based on the behavior change information, wherein determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle is based on the updated respective likelihoods.

5. The method of claim 1, further comprising:

comparing the location, speed, and pose of the second vehicle relative to the first vehicle and the other objects to a predetermined driving scenario among a plurality of predetermined driving scenarios stored in a memory coupled to the computing system, wherein estimating the one or more possible future behaviors of the second vehicle comprises determining the one or more possible future behaviors based on a predetermined response associated with the predetermined driving scenario.

6. The method of claim 1, wherein determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle comprises:

determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle so as avoid collision with the second vehicle.

7. The method of claim 1, wherein the one or more possible future behaviors of the second vehicle comprise the second vehicle changing one or more of the speed, the pose, and lane of travel of the second vehicle.

8. The method of claim 1, wherein the computing system is configured to control the vehicle in an autonomous driving mode.

9. The method of claim 1, wherein determining to modify the one or more of the speed, pose, and lane of travel of the first vehicle comprises:

performing at least one of accelerating, decelerating, changing heading, changing lanes, shifting within a current lane, or providing a warning notification.

10. A non-transitory computer readable medium having stored thereon instructions that, when executed by a computer device configured to control a first vehicle, cause the computer device to perform functions comprising:

receiving vehicle information indicative of lane of travel, speed, and pose of the first vehicle, wherein the pose of the first vehicle includes heading and orientation of the first vehicle;

estimating one or more possible future behaviors of a second vehicle in a vicinity of the first vehicle based on location, speed, and pose of the second vehicle relative to the first vehicle and other objects in the vicinity of the first vehicle;

determining respective likelihoods of occurrence for the one or more possible future behaviors of the second vehicle; and

based on the respective likelihoods, determining whether to maintain or modify one or more of the speed, pose, and lane of travel of the first vehicle.

11. The non-transitory computer readable medium of claim 10, wherein the function of estimating the one or more possible future behaviors comprises:

estimating the one or more future behaviors further based on road geometry and road condition of a road of travel of the first vehicle.

12. The non-transitory computer readable medium of claim 10, wherein the function of estimating the one or more possible future behaviors comprises:

estimating the one or more possible future behaviors further based on a predicted behavior of the second vehicle in response to an action of the first vehicle.

13. The non-transitory computer readable medium of claim 10, wherein the functions further comprise:

receiving behavior change information indicating a change in behavior of one or more of the first vehicle, the second vehicle, and the other objects; and

updating the respective likelihoods based on the behavior change information, wherein determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle is based on the updated respective likelihoods.

14. The non-transitory computer readable medium of claim 10, wherein the functions further comprise:

comparing the location, speed, and pose of the second vehicle relative to the first vehicle and the other objects to a predetermined driving scenario among a plurality of predetermined driving scenarios stored in a memory coupled to the computing device, wherein the function of estimating the one or more possible future behaviors of the second vehicle comprises determining the one or more possible future behaviors based on a predetermined response associated with the predetermined driving scenario.

15. The non-transitory computer readable medium of claim 10, wherein the function of determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle comprises:

determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle so as avoid collision with the second vehicle.

16. The non-transitory computer readable medium of claim 10, wherein the one or more possible future behaviors of the second vehicle comprise the second vehicle changing one or more of speed, pose, and lane of travel of the second vehicle.

17. A system, comprising:

at least one processor; and

data storage comprising instructions that, when executed by the at least one processor, cause the system to perform functions comprising;

receiving vehicle information indicative of lane of travel, speed, and pose of the first vehicle, wherein the pose of the first vehicle includes heading and orientation of the first vehicle;

estimating one or more possible future behaviors of a second vehicle in a vicinity of the first vehicle based on location, speed, and pose of the second vehicle relative to the first vehicle and other objects in the vicinity of the first vehicle;

determining respective likelihoods of occurrence for the one or more possible future behaviors of the second vehicle; and

based on the respective likelihoods, determining whether to maintain or modify one or more of the speed, pose, and lane of travel of the first vehicle.

18. The system of claim 17, wherein the functions further comprise:

receiving behavior change information indicating a change in behavior of one or more of the first vehicle, the second vehicle, and the other objects; and

updating the respective likelihoods based on the behavior change information, wherein determining whether to maintain or modify the one or more of the speed, pose, and lane of travel of the first vehicle is based on the updated respective likelihoods.

19. The system of claim 17, wherein the function of estimating the one or more possible future behaviors comprises:

estimating the one or more possible future behaviors further based on a predicted behavior of the second vehicle in response to an action of the first vehicle.

20. The system of claim 17, wherein the function of estimating the one or more possible future behaviors comprises:

estimating the one or more future behaviors further based on road geometry and road condition of a road of travel of the first vehicle.