Approach for consolidating observed vehicle trajectories into a single representative trajectory

• US 9,459,625 B1
• Filed: 08/01/2014
• Issued: 10/04/2016
• Est. Priority Date: 03/16/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ferguson, David Ian Franklin, Zhu, Jiajun
• Primary Examiner(s): Hilgendorf, Dale W
• Application Number:
  US14/449,792
• Time in Patent Office: 795 Days
• US Class Current: 1/1
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/20 including control of steering systems
• CPC Class Codes: B60W 2050/048 displaying data transmitted between vehicles, e.g. for platooning, control of inter-vehicle distance
• CPC Class Codes: B60W 2554/408 Traffic behavior, e.g. swarm
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/072 Curvature of the road
• 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: B62D 1/00 Steering controls, i.e. means for initiating a change of direction of the vehicle
• CPC Class Codes: B62D 15/00 Steering not otherwise provided for
• CPC Class Codes: B62D 15/025 Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation B62D1/28 takes precedence
• CPC Class Codes: B62D 15/0255 Automatic changing of lane, e.g. for passing another vehicle
• CPC Class Codes: B62D 15/026 combined with automatic distance control, i.e. electronic tow bar
• CPC Class Codes: B62D 6/00 Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits means for initiating a change in direction B62D1/00; steering valves B62D5/06; combined with means for inclining the vehicle body or wheels on bends B62D9/00
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 15/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S15/46
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/88 : Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 2013/9325 : for inter-vehicle distance regulation, e.g. navigating in platoons
• CPC Class Codes: G01S 2205/002 : for traffic control, mobile tracking, guidance, surveillance or anti-collision
• 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 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0214 : in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/0223 : involving speed control of the vehicle vehicle fittings for automatically controlling, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator B60K31/00
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0287 : involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G05D 1/0293 : Convoy travelling
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: G08G 1/22 : Platooning, i.e. convoy of communicating vehicles

Abstract

A method and apparatus is provided for controlling the operation of an autonomous vehicle. According to one aspect, the autonomous vehicle may track the trajectories of other vehicles on a road. Based on the other vehicle'"'"'s trajectories, the autonomous vehicle may generate a pool of combined trajectories. Subsequently, the autonomous vehicle may select one of the combined trajectories as a representative trajectory. The representative trajectory may be used to change at least one of the speed or direction of the autonomous vehicle.

Claims

1. A method comprising:

tracking, with one or more processors, a plurality of road vehicles to obtain a plurality of observed trajectories, each one of the plurality of observed trajectories including locations passed by a given road vehicle from the plurality of road vehicles;

selecting, with the one or more processors, one of the plurality of observed trajectories as a base;

combining, with the one or more processors, the selected observed trajectory with one or more remaining trajectories in the plurality of observed trajectories;

generating, with the one or more processors, a new trajectory based on the combined trajectories; and

changing, with the one or more processors, at least one of a speed of the given road vehicle and a direction of the given road vehicle based on the new trajectory.

2. The method of claim 1, wherein combining the selected observed trajectory comprises identifying, for each location in the selected observed trajectory, locations in remaining ones of the plurality of observed trajectories that are within a predetermined distance from the location in the selected observed trajectory.

3. The method of claim 1, wherein the plurality of generated trajectories is generated using an expectation-maximization (EM) process.

4. The method of claim 1, further comprising:

generating a plurality of new trajectories, wherein each of the plurality of generated trajectories uses a different observed trajectory as the base; and

selecting a first trajectory from the plurality of new generated trajectories.

5. The method of claim 4, wherein the selected first trajectory is selected based on a discontinuity in the selected first trajectory.

6. The method of claim 4, wherein the selected first trajectory is selected based on a characteristic of a curvature of the selected first trajectory.

7. The method of claim 4, further comprising:

determining a current location of the vehicle; and

wherein the selected first trajectory is selected based on a distance between the current location and a location that is part of the selected first trajectory.

8. A non-transitory computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:

tracking a plurality of road vehicles to obtain a plurality of observed trajectories, each one of the plurality of observed trajectories including locations passed by a given road vehicle from the plurality of road vehicles;

selecting one of the plurality of observed trajectories as a base;

combining the selected observed trajectory with remaining ones in the plurality of observed trajectories;

generating a new trajectory based on the combined trajectories; and

changing at least one of a speed of the given road vehicle and a direction of the given road vehicle based on the new trajectory.

9. The medium of claim 8, wherein combining the selected observed trajectory comprises identifying, for each location in the selected observed trajectory, locations in remaining ones of the plurality of observed trajectories that are within a predetermined distance from the location in the selected observed trajectory.

10. The medium of claim 8, wherein the plurality of generated trajectories is generated using an expectation-maximization (EM) process.

11. The medium of claim 8, further comprising:

generating a plurality of new trajectories, wherein each of the plurality of generated trajectories uses a different observed trajectory as the base; and

selecting a first trajectory from the plurality of new generated trajectories.

12. The medium of claim 11, wherein the selected first trajectory is selected based on a discontinuity in the selected first trajectory.

13. The medium of claim 11, wherein the selected first trajectory is selected based on a characteristic of a curvature of the selected first trajectory.

14. The medium of claim 11, further comprising:

determining a current location of the vehicle; and

wherein the selected first trajectory is selected based on a distance between the current location and a location that is part of the selected first trajectory.

15. A system comprising:

memory;

one or more processors in communication with the memory, the one or more processors configured to;

track a plurality of road vehicles to obtain a plurality of observed trajectories, each one of the plurality of observed trajectories including locations passed by a given road vehicle from the plurality of road vehicles;

select one of the plurality of observed trajectories as a base;

combine the selected observed trajectory with remaining ones in the plurality of observed trajectories;

generate a new trajectory based on the combined trajectories; and

change at least one of a speed of the given road vehicle and a direction of the given road vehicle based on the new trajectory.

16. The system of claim 15, wherein combining the selected observed trajectory comprises identifying, for each location in the selected observed trajectory, locations in remaining ones of the plurality of observed trajectories that are within a predetermined distance from the location in the selected observed trajectory.

17. The system of claim 15, wherein the plurality of generated trajectories is generated using an expectation-maximization (EM) process.

18. The system of claim 15, wherein the one or more processors are further configured to:

generate a plurality of new trajectories, wherein each of the plurality of generated trajectories uses a different observed trajectory as the base; and

select a first trajectory from the plurality of new generated trajectories.

19. The system of claim 18, wherein the selected first trajectory is selected based on a discontinuity in the selected first trajectory.

20. The system of claim 18, wherein the selected first trajectory is selected based on a characteristic of a curvature of the selected first trajectory.