VEHICLE TRAJECTORY DETERMINATION

• US 20170031361A1
• Filed: 07/31/2015
• Published: 02/02/2017
• Est. Priority Date: 07/31/2015
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company), Board of Regents of the University of Michigan (University of Michigan), University of Michigan
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company), Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Cunningham, Alexander G., Olson, Edwin, Galceran, Enric, Eustice, Ryan M., McBride, James Robert
• Granted Patent: US 9,934,688 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 2050/0031 Mathematical model of the vehicle
• CPC Class Codes: B60W 2050/006 Interpolation; Extrapolation
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

A system includes a computer programmed to identify, from a first vehicle, one or more second vehicles within a specified distance to the first vehicle. The computer is further programmed to receive data about operations of each of the second vehicles, including trajectory data. Based on the data, the computer is programmed to identify, for each of the second vehicles, a distribution of probabilities of each of a set of potential planned trajectories. The computer is further programmed to determine a planned trajectory for the first vehicle, based on the respective distributions of probabilities of each of the set of potential planned trajectories for each of the second vehicles. The computer is further programmed to provide an instruction to at least one controller associated with the first vehicle based on the determined planned trajectory.

Claims

1. A system comprising a computer including a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to:

identify, from a first vehicle, one or more second vehicles within a specified distance to the first vehicle;

receive data about operations of each of the one or more second vehicles, the data including at least trajectory data for each of the second vehicles;

identify, based at least in part on the data, a distribution of probabilities for each second vehicle;

each of the probabilities associated with a potential planned trajectory of the respective vehicle;

determine a planned trajectory for the first vehicle, based at least in part on the respective distributions of probabilities of each of the set of potential planned trajectories of each of the one or more second vehicles; and

provide an instruction to at least one controller associated with the first vehicle based on the determined planned trajectory.

2. The system of claim 1, wherein the planned trajectory further indicates one or more alternative trajectories, each of the alternative trajectories associated with potential planned trajectories of the one or more second vehicles.

3. The system of claim 1, wherein the computer is further programmed to:

identify one or more candidate planned trajectories for the first vehicle;

perform one or more forward simulations based at least in part on the candidate planned trajectories for the first vehicle and the distribution of probabilities for each second vehicle, wherein determining the planned trajectory for the first vehicle is further based in part on results of the one or more forward simulations.

4. The system of claim 3, further wherein performing each of the one or more forward simulations includes:

selecting a sample planned trajectory for the first vehicle from the candidate planned trajectories;

selecting a sample planned trajectory for each of the one or more second vehicles from the respective set of potential planned trajectories; and

performing the forward simulation based on the selected sample planned trajectories for the first and one or more second vehicles.

5. The system of claim 4, wherein the computer is further programmed to:

identify one or more rewards reflecting a desired result;

wherein determining the planned trajectory for the first vehicle is further based in part on the identified rewards.

6. The system of claim 5, wherein the computer is further programmed to:

compare a result of the one or more forward simulations with respect to each of the identified one or more rewards;

assign, based at least in part on the comparison, a weighted value to the result of the forward simulation with respect to each of the identified one or more rewards;

compute a sum of the weighted values of the result for each of the one or more forward simulations with respect to each of the identified one or more rewards; and

identify a forward simulation from the one or more forward simulations with a highest sum, wherein determining the planned trajectory for the first vehicle is based at least in part on the selected sample planned trajectory used for the first vehicle in the identified forward simulation.

7. The system of claim 1, wherein the computer further programmed to:

identify change points in the trajectory of each of the respective second vehicles, the change points indicating points along the trajectory where it is determined that the planned trajectory for the second vehicle changed from one planned trajectory to another planned trajectory.

8. The system of claim 7, wherein the computer is further programmed to:

identify segments between each adjacent pair of change points along the respective path for each second vehicle; and

determine a latent planned trajectory that fits to the trajectory of the respective second vehicle in each identified segment.

9. The system of claim 8, wherein identifying the respective distributions of probabilities of for each of the one or more second vehicles is based on the determined latent planned trajectories for each segment along the path of the respective second vehicle.

10. The system of claim 1, wherein the computer is further programmed to:

determine, based on the data, that the behavior of one of the one or more second vehicles cannot be explained by any of a set of available planned trajectories.

11. A method comprising:

identifying, from a first vehicle, one or more second vehicles within a specified distance to the first vehicle;

receiving data about operations of each of the one or more second vehicles, the data including at least trajectory data for each of the second vehicles;

identifying, based at least in part on the data, a distribution of probabilities for each second vehicle;

each of the probabilities associated with a potential planned trajectory of the respective vehicle;

determining a planned trajectory for the first vehicle, based at least in part on the respective distributions of probabilities of each of the set of potential planned trajectories of each of the one or more second vehicles; and

provide an instruction to at least one controller associated with the first vehicle based on the determined planned trajectory.

12. The method of claim 11, wherein the planned trajectory further indicates one or more alternative trajectories, each of the alternative trajectories associated with potential planned trajectories of the one or more second vehicles.

13. The method of claim 11, further comprising:

identifying one or more candidate planned trajectories for the first vehicle;

performing one or more forward simulations based at least in part on the candidate planned trajectories for the first vehicle and the distribution of probabilities for each of the one or more second vehicles, wherein determining the planned trajectory for the first vehicle is further based in part on results of the one or more forward simulations.

14. The method of claim 13, wherein performing each of the one or more forward simulations includes:

selecting a sample planned trajectory for the first vehicle from the candidate planned trajectories;

selecting a sample planned trajectory for each of the one or more second vehicles from the respective set of potential planned trajectories; and

performing the forward simulation based on the selected sample planned trajectories for the first and one or more second vehicles.

15. The method of claim 14, further comprising:

identifying one or more rewards reflecting a desired result;

wherein determining the planned trajectory for the first vehicle is further based in part on the identified rewards.

16. The method of claim 15, further comprising:

comparing a result of the one or more forward simulations with respect to each of the identified one or more rewards;

assigning, based at least in part on the comparison, a weighted value to the result of the forward simulation with respect to each of the identified one or more rewards;

computing a sum of the weighted values of the result for each of the one or more forward simulations with respect to each of the identified one or more rewards; and

identifying a forward simulation from the one or more forward simulations with a highest sum, wherein determining the planned trajectory for the first vehicle is based at least in part on the selected sample planned trajectory used for the first vehicle in the identified forward simulation.

17. The method of claim 11, further comprising:

identifying change points in the trajectory of each of the respective second vehicles, the change points indicating points along the trajectory where it is determined that the planned trajectory for the second vehicle changed from one planned trajectory to another planned trajectory.

18. The method of claim 17, further comprising:

identifying segments between each adjacent pair of change points along the respective path for each second vehicle; and

determining a latent planned trajectory that fits to the trajectory of the respective second vehicle in each identified segment.

19. The method of claim 18, wherein identifying the respective distributions of probabilities for each of the one or more second vehicles is based on the determined latent planned trajectories for each segment along the path of the respective second vehicle.

20. The method of claim 11, further comprising:

determining, based on the data, that the behavior of one of the one or more second vehicles cannot be explained by any of a set of available planned trajectories.