Position Estimation and Vehicle Control in Autonomous Multi-Vehicle Convoys

• US 20140309836A1
• Filed: 01/21/2014
• Published: 10/16/2014
• Est. Priority Date: 04/16/2013
• Status: Abandoned Application

• Current Assignee: Neya Systems, LLC (Applied Research Associates Incorporated)
• Original Assignee: Neya Systems, LLC (Applied Research Associates Incorporated)
• Inventors: Ollis, Mark Daniel
• Application Number:
  US14/160,524
• Publication Number:
  US 20140309836A1
• Time in Patent Office: Days
• US Class Current: 701/25
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: G08G 1/22 Platooning, i.e. convoy of communicating vehicles

Abstract

Techniques are provided for providing position estimations in an autonomous multi-vehicle convoy. Those techniques include initializing a convoy state, selecting a next sensor reading; predicting a convoy state, updating the convoy state, and broadcasting the convoy state to vehicles in the multi-vehicle convoy.

Claims

1. A computer-implemented method for providing pose estimations in a multi-vehicle convoy, the method comprising:

initializing, using a convoy control system, a convoy state, wherein the convoy state comprises pose data for each vehicle of the multi-vehicle convoy or pose data for a subset of the vehicles of the multi-vehicle convoy;

selecting, using the convoy control system, a sensor reading data from at least one vehicle sensor amongst a plurality of vehicle sensors;

updating, using the convoy control system, a future convoy state based on the sensor reading data;

predicting, using the convoy control system, the convoy state to a future point in time; and

communicating the updated convoy state for affecting future pose of the multi-vehicle convoy.

2. The computer-implemented method of claim 1, comprising:

determining which vehicles in the multi-vehicle convoy are affected by the updated convoy state; and

communicating the updated convoy state to the affected vehicles.

3. The computer-implemented method of claim 1, comprising communicating the updated convoy state to all vehicles in the multi-vehicle convoy.

4. The computer-implemented method of claim 1, comprising communicating the updated convoy state to a subset of vehicles in the multi-vehicle convoy.

5. The computer-implemented method of claim 1, comprising communicating the updated convoy state to all vehicles in the multi-vehicle convoy.

6. The computer-implemented method of claim 1, wherein the at least one vehicle sensor comprises a pose sensor, Global Positioning System (GPS) indicator, ranging sensor, bearing sensor, or position sensor.

7. The computer-implemented method of claim 1, wherein the at least one vehicle sensor comprises a pose sensor for at least one of the vehicles in the multi-vehicle convoy.

8. The computer-implemented method of claim 1, further comprising:

obtaining control sequence data from at least one of the vehicles in the multi-vehicle convoy; and

updating the convoy state based on the predicted future convoy state and the control sequence data.

9. The computer-implemented method of claim 8, further comprising:

in response to the updated convoy state, determining which vehicles in the multi-vehicle convoy are affected by the updated convoy state;

determining updated control sequence data for the affected vehicles; and

communicating the updated control sequence data to the affected vehicles.

10. The computer-implemented method of claim 9, wherein determining the updated control sequence data for the affected vehicles comprises performing a gradient descent on the obtained control sequence data.

11. The computer-implemented method of claim 9, wherein determining the updated control sequence data for the affected vehicles comprises applying a convoy vehicle model to the obtained control sequence data.

12. The computer-implemented method of claim 1, wherein predicting the future convoy state based on the sensor reading data comprising applying an extended Kalman filter to the sensor reading data.

13. The computer-implemented method of claim 1, wherein the convoy control system is centralized control system.

14. The computer-implemented method of claim 1, wherein the convoy control system is a distributed control system.

15. The computer-implemented method of claim 1, wherein the distributed control system operates is a point-to-point manner.

16. The computer-implemented method of claim 1, wherein the distributed control system operates as a mesh network.

17. A vehicle of a multi-vehicle convoy, the vehicle comprising:

a convoy control system having a processor, a memory and a communications interface, wherein the convoy control system configured to

initialize a convoy state, wherein the convoy state comprises pose data for each vehicle of the multi-vehicle convoy or pose data for a subset of the vehicles of the multi-vehicle convoy;

select a sensor reading data from at least one vehicle sensor amongst a plurality of vehicle sensors;

update a future convoy state based on the sensor reading data;

predict the convoy state to a future point in time; and

communicate the updated convoy state for affecting future pose of the multi-vehicle convoy.

18. The vehicle of claim 17, wherein the convoy control system is further configured to

determine which vehicles in the multi-vehicle convoy are affected by the updated convoy state;

and

communicate the updated convoy state to the affected vehicles.

19. The vehicle of claim 17, wherein the convoy control system is further configured to

obtain control sequence data from at least one of the vehicles in the multi-vehicle convoy;

and

update the convoy state based on the predicted future convoy state and the control sequence data.

20. The vehicle of claim 17, wherein the convoy control system is further configured to

in response to the updated convoy state, determine which vehicles in the multi-vehicle convoy are affected by the updated convoy state;

determine updated control sequence data for the affected vehicles; and

communicate the updated control sequence data to the affected vehicles.

21. The vehicle of claim 20, wherein the convoy control system is configured to determine the updated control sequence data for the affected vehicles by performing a gradient descent on the obtained control sequence data.

22. The vehicle of claim 20, wherein the convoy control system is configured to determine the updated control sequence data for the affected vehicles by applying a convoy vehicle model to the obtained control sequence data.

23. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for providing pose estimations in a multi-vehicle convoy, the instructions comprising:

instructions for initializing, using a convoy control system, a convoy state, wherein the convoy state comprises pose data for each vehicle of the multi-vehicle convoy or pose data for a subset of the vehicles of the multi-vehicle convoy;

instructions for selecting, using the convoy control system, a sensor reading data from at least one vehicle sensor amongst a plurality of vehicle sensors;

instructions for updating, using the convoy control system, a future convoy state based on the sensor reading data;

instructions for predicting, using the convoy control system, the convoy state to a future point in time; and

instructions for communicating the updated convoy state for affecting future pose of the multi-vehicle convoy.

24. The computer-readable storage medium of claim 23, having stored thereon a set of further instructions comprising:

instructions for determining which vehicles in the multi-vehicle convoy are affected by the updated convoy state; and

instructions for communicating the updated convoy state to the affected vehicles.

25. The computer-readable storage medium of claim 23, having stored thereon a set of further instructions comprising:

instructions for determining which vehicles in the multi-vehicle convoy are affected by the updated convoy state; and

instructions for communicating the updated convoy state to the affected vehicles.

26. The computer-readable storage medium of claim 23, having stored thereon a set of further instructions comprising:

instructions for obtaining control sequence data from at least one of the vehicles in the multi-vehicle convoy; and

instructions for updating the convoy state based on the predicted future convoy state and the control sequence data.

27. The computer-readable storage medium of claim 23, having stored thereon a set of further instructions comprising:

instructions for, in response to the updated convoy state, determining which vehicles in the multi-vehicle convoy are affected by the updated convoy state;

instructions for determining updated control sequence data for the affected vehicles; and

instructions for communicating the updated control sequence data to the affected vehicles.

28. The computer-readable storage medium of claim 27, having stored thereon a set of further instructions for determining the updated control sequence data for the affected vehicles by performing a gradient descent on the obtained control sequence data.

29. The computer-readable storage medium of claim 27, having stored thereon a set of further instructions for determining the updated control sequence data for the affected vehicles by applying a convoy vehicle model to the obtained control sequence data.

30. A computer-implemented method for determining a control sequence for a follower vehicle in a multi-vehicle convoy, the method comprising:

a) receiving, at a convoy control system, a control sequence for a lead vehicle;

b) estimating, at the convoy control system, a vehicle path for the lead vehicle;

c) initializing a current best control sequence equal to the lead vehicle control sequence;

d) forward simulating, at the convoy control system, a vehicle control sequence for the follower vehicle based on the current best vehicle control sequence, generating a forward simulated path;

e) measuring the error between the forward simulated vehicle path and the lead vehicle path;

f) performing a gradient descent operation on the forward simulated vehicle control sequence;

g) analyzing the forward simulated vehicle control sequence for convergence with the lead vehicle path;

if the forward simulated control sequence does not converge with the vehicle path, setting the current best control sequence equal to the forward simulated vehicle control sequence then repeating d), e), f) and g); and

if the forward simulated control sequence does converge with the vehicle path, issuing the forward simulated control sequence to the follower vehicle.

31. The computer-implemented method of claim 30, wherein estimating the lead vehicle path further comprises:

receiving, at a convoy control system, one or more pose sensor readings for the lead vehicle;

loading, at the convoy control system, a vehicle model for the lead vehicle;

using an extended Kalman filter to determine the lead vehicle'"'"'s current best pose estimate based on the lead vehicle'"'"'s sensor readings and the vehicle model for the lead vehicle; and

using a fixed-lag Kalman smoother to improve the lead vehicle'"'"'s position estimate over a previous period of time based on the vehicle model.

32. The computer-implemented method of claim 31, wherein the previous period of time is equal to the convoy length divided by the convoy speed.

33. A vehicle of a multi-vehicle convoy, the vehicle comprising:

a convoy control system having a processor, a memory and a communications interface, wherein the convoy control system configured to

a) receive, at a convoy control system, a control sequence for a lead vehicle;

b) estimate, at the convoy control system, a vehicle path for the lead vehicle;

c) initialize a current best control sequence equal to the lead vehicle control sequence;

d) forward simulate, at the convoy control system, a vehicle control sequence for the follower vehicle based on the current best vehicle control sequence, generating a forward simulated path;

e) measure the error between the forward simulated vehicle path and the lead vehicle path;

f) perform a gradient descent operation on the forward simulated vehicle control sequence;

g) analyze the forward simulated vehicle control sequence for convergence with the lead vehicle path;

if the forward simulated control sequence does not converge with the vehicle path, set the current best control sequence equal to the forward simulated vehicle control sequence then repeating d), e), f) and g); and

if the forward simulated control sequence does converge with the vehicle path, issue the forward simulated control sequence to the follower vehicle.

34. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for determining a control sequence for a follower vehicle in a multi-vehicle convoy, the instructions comprising:

a) instructions for receiving, at a convoy control system, a control sequence for a lead vehicle;

b) instructions for estimating, at the convoy control system, a vehicle path for the lead vehicle;

c) instructions for initializing a current best control sequence equal to the lead vehicle control sequence;

d) instructions for forward simulating, at the convoy control system, a vehicle control sequence for the follower vehicle based on the current best vehicle control sequence, generating a forward simulated path;

e) instructions for measuring the error between the forward simulated vehicle path and the lead vehicle path;

f) instructions for performing a gradient descent operation on the forward simulated vehicle control sequence;

g) instructions for analyzing the forward simulated vehicle control sequence for convergence with the lead vehicle path;

instructions for, if the forward simulated control sequence does not converge with the vehicle path, setting the current best control sequence equal to the forward simulated vehicle control sequence then repeating d), e), f) and g); and

instructions for, if the forward simulated control sequence does converge with the vehicle path, issuing the forward simulated control sequence to the follower vehicle.

35. A computer-implemented method for determining a control sequence for a follower vehicle in a multi-vehicle convoy, the method comprising:

initializing a vehicle parameter model comprising a plurality of parameters for a vehicle;

obtaining log data for the vehicles whose parameters are to be learned, the log data comprising control data and actual pose data for the vehicle over a period of time;

determining a vehicle model error from comparing estimated pose data and the actual pose data for the multi-vehicle convoy; and

in response to determining the vehicle model error, updating the vehicle parameter model to reduce the vehicle model error.

36. A vehicle of a multi-vehicle convoy, the vehicle comprising:

a convoy control system having a processor, a memory and a communications interface, wherein the convoy control system configured to

initialize a vehicle parameter model comprising a plurality of parameters for a vehicle;

obtain log data for the vehicles whose parameters are to be learned, the log data comprising control data and actual pose data for the vehicle over a period of time;

determine a vehicle model error from comparing estimated pose data and the actual pose data for the multi-vehicle convoy; and

in response to determining the vehicle model error, update the vehicle parameter model to reduce the vehicle model error.

37. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for determining a control sequence for a follower vehicle in a multi-vehicle convoy, the instructions comprising:

instructions for initializing a vehicle parameter model comprising a plurality of parameters for a vehicle;

instructions for obtaining log data for the vehicles whose parameters are to be learned, the log data comprising control data and actual pose data for the vehicle over a period of time;

instructions for determining a vehicle model error from comparing estimated pose data and the actual pose data for the multi-vehicle convoy; and

instructions for, in response to determining the vehicle model error, updating the vehicle parameter model to reduce the vehicle model error.