Game-theoretic planning for risk-aware interactive agents

Patent metadata

Field	Value
Publication number	US-2022032960-A1
Application number	US-202016942560-A
Country	US
Kind code	A1
Filing date	Jul 29, 2020
Priority date	Jul 29, 2020
Publication date	Feb 3, 2022
Grant date	—

Abstract

A method for risk-aware game-theoretic trajectory planning is described. The method includes modeling an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment. The method also includes ranking upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories. The method further includes selecting a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.

First claim

What is claimed is:

1 . A method for risk-aware game-theoretic trajectory planning, the method comprising:
modeling an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment; ranking upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories; and selecting a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.

2 . The method of claim 1 , in which modeling comprises:
approximating a feedback Nash equilibria of a risk-aware game-theoretic trajectory planning; deriving, at each iteration, a linearized approximation of system dynamics and a quadratic approximation of the risk-aware cost function; and solving a backward recursion for finding the feedback Nash equilibria of the risk-aware game-theoretic trajectory planning.

3 . The method of claim 1 , in which ranking the upcoming planned trajectories comprises:
computing risk-sensitive cost functions associated with the upcoming planned trajectories relative to the other vehicle; and planning the vehicle trajectory of the ego vehicle based on the risk-sensitive cost functions associated with the planned trajectories relative to the other vehicle and the risk-sensitivity of the other vehicle.

4 . The method of claim 3 , further comprising accelerating a speed of the ego vehicle to successfully merge into a target lane of a multilane roadway including the other vehicle.

5 . The method of claim 3 , further comprising decelerating a speed of the ego vehicle to successfully merge into a target lane of a multilane roadway including the other vehicle.

6 . The method of claim 5 , in which the ego vehicle is on an on-ramp of the multilane roadway and the target lane is a first lane of the multilane roadway.

7 . The method of claim 1 , further comprising discarding an upcoming planned trajectory if the risk-aware cost function of the upcoming planned trajectory is greater than a predetermined value.

8 . The method of claim 1 , further comprising discarding an upcoming planned trajectory if the risk-sensitivity of the other vehicle corresponding to the upcoming planned trajectory is less than a predetermined value.

9 . The method of claim 1 , further comprising determining the risk-sensitivity of the other vehicle using vehicle-to-vehicle (V2V) communication between the ego vehicle and the other vehicle.

10 . A non-transitory computer-readable medium having program code recorded thereon for risk-aware game-theoretic trajectory planning, the program code being executed by a processor and comprising:
program code to model an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment; program code to rank upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories; and program code to select a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.

11 . The non-transitory computer-readable medium of claim 10 , in which the program code to model comprises:
program code to approximate a feedback Nash equilibria of the risk-aware game-theoretic trajectory planning; program code to derive, at each iteration, a linearized approximation of system dynamics and a quadratic approximation of the risk-aware cost function; and program code to solve a backward recursion for finding the feedback Nash equilibria of the risk-aware game-theoretic trajectory planning.

12 . The non-transitory computer-readable medium of claim 10 , in which the program code to rank the upcoming planned trajectories comprises:
program code to compute risk-sensitive cost functions associated with the upcoming planned trajectories relative to the other vehicle; and program code to plan the vehicle trajectory of the ego vehicle based on the risk-sensitive cost functions associated with the upcoming planned trajectories relative to the other vehicle and the risk-sensitivity of the other vehicle.

13 . The non-transitory computer-readable medium of claim 12 , further comprising program code to accelerate a speed of the ego vehicle to successfully merge into a target lane of a multilane roadway including the other vehicle.

14 . The non-transitory computer-readable medium of claim 12 , further comprising program code to decelerate a speed of the ego vehicle to successfully merge into a target lane of a multilane roadway including the other vehicle.

15 . The non-transitory computer-readable medium of claim 14 , in which the ego vehicle is on an on-ramp of the multilane roadway and the target lane is a first lane of the multilane roadway.

16 . The non-transitory computer-readable medium of claim 10 , further comprising program code to discard an upcoming planned trajectory if the risk-aware cost function of the upcoming planned trajectory is greater than a predetermined value and/or if the risk-sensitivity of the other vehicle corresponding to the upcoming planned trajectory is less than a predetermined value.

17 . A system for risk-aware game-theoretic trajectory planning, the system comprising:
a game-theoretic risk model configured to model an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment; a risk-aware cost function module configured to rank upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories; and a vehicle trajectory selection module configured to select a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.

18 . The system of claim 17 , further comprising an ego perception module to determine a current trajectory of the ego vehicle and the current trajectory of the other vehicle.

19 . The system of claim 18 , further comprising a vehicle trajectory planner to plan the upcoming planned trajectories of the ego vehicle according to the current trajectory of the ego vehicle and the current trajectory of the other vehicle.

20 . The system of claim 17 , in which the game-theoretic risk model is configured:
to approximate a feedback Nash equilibria of the risk-aware game-theoretic trajectory planning; to derive, at each iteration, a linearized approximation of system dynamics and a quadratic approximation of the risk-aware cost function; and to solve a backward recursion for finding the feedback Nash equilibria of the risk-aware game-theoretic trajectory planning.

Assignees

• Toyota Res Inst Inc
• Univ Leland Stanford Junior

Inventors

• Wang Mingyu
• Zahedi Mehr Negar
• Gaidon Adrien David
• Schwager Mac

Classifications

• H04W4/40 Primary

  for vehicles, e.g. vehicle-to-pedestrians [V2P] · CPC title

• G05D1/0214

  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 title

• G05D1/0088 Primary

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title

• G01C21/3453

  Special cost functions, i.e. other than distance or default speed limit of road segments · CPC title

• B60W2556/65

  Data transmitted between vehicles · CPC title