Adversarial scenarios for safety testing of autonomous vehicles

Patent metadata

Field	Value
Publication number	US-11550325-B2
Application number	US-202016898308-A
Country	US
Kind code	B2
Filing date	Jun 10, 2020
Priority date	Jun 10, 2020
Publication date	Jan 10, 2023
Grant date	Jan 10, 2023

Abstract

Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

First claim

What is claimed is:

1. A method for modifying the behavioral logic of an autonomous vehicle, the method comprising:
assigning a narrowness metric to each of one or more paths in a driving scenario;
assigning an effort metric to the paths;
assigning an amount of time for action by the autonomous vehicle to avoid collision along the paths;
updating the behavioral logic of the autonomous vehicle based on a scoring of the paths;
wherein the effort metric of the path comprises a sum of efforts for nodes of the path and the effort of a node in the path comprises a sum of absolute values of steering and acceleration required to reach the node from a parent node of the path.

2. The method of claim 1 , wherein the paths are generated by introducing perturbations into the driving scenario.

3. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
characterize a path in a driving scenario according to narrowness and effort of the path;
assign to each node of the path a minimum amount of time for action by a vehicle to avoid collision from the node;
simulate the path in the computer; and
wherein the effort of the path comprises a sum of efforts for nodes of the path and the effort of a node in the path comprises a sum of absolute values of steering and acceleration required to reach the node from a parent node of the path.

4. The computer-readable storage medium of claim 3 , the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
score the path based at least in part on the effort of the path.

5. A method for computer simulation of a driving scenario for an autonomous vehicle, the method comprising:
configuring the driving scenario with a set of road descriptors on which a set of actors operate, each actor assigned a behavioral policy, and wherein at least one actor is the autonomous vehicle;
deriving a multivariate metric for a number of safe driving paths in the driving scenario;
assigning a narrowness metric to each of the paths in the driving scenario;

assigning an effort metric to nodes of the paths, wherein the effort metric of a node in the driving scenario is determined as a sum of absolute values of steering and acceleration required to reach the node from a parent node;
assigning to each node a minimum amount of time for action by the autonomous vehicle to avoid collision from the node;
executing the driving scenario in the computer simulation; and
improving a behavioral policy of the autonomous vehicle based on results of the computer simulation.

6. The method of claim 5 further comprising:
generating unsafe paths into the driving scenario by introducing perturbations into the driving scenario.

7. The method of claim 5 further comprising:
scoring the driving scenario based on performance of the autonomous vehicle in the computer simulation;
prioritizing the driving scenario based on the scoring; and
applying the prioritization to improve the behavioral policy of the autonomous vehicle.

8. The method of claim 5 wherein the effort for a path is computed as a sum of node efforts in the path.

9. The method of claim 5 wherein the multivariate metric comprises:
a total number of paths to evaluate from a beginning of the driving scenario to an end of the driving scenario, wherein the total number of paths forms a tree comprising branches;
a number of no-collision driving paths from the beginning of the driving scenario to the end of the driving scenario;
an average effort for the no-collision driving paths;
a minimum no-collision driving path effort;
a minimum amount of time before a collision occurs in a branch of the tree comprising at least one no-collision driving path; and
an average of a minimum number of branches of the tree along each no-collision driving path.

10. The method of claim 5 wherein:
a rate of change of a state of an actor is defined as a control policy represented by

f
=

ds
dt

=

[

a
⁢

υ
⁢

tan
⁡
(
δ
)

L

]

wherein a is an acceleration of the actor, δ is a steering angle of the actor, L is a wheelbase of the actor, and v is a scalar velocity of the actor.

11. The method of claim 5 further comprising:
identifying a control policy for an unsafe actor A;
applying the control policy for the unsafe actor to generate a trajectory that minimizes an objective function γ represented by

γ
AE

=

D
AE
2

=



x
A

-

x
E



2
2

=

(

x
iA

-

x
iE

)

2

+

(

x
jA

-

x
jE

)

2

wherein a position of actors A and E in the driving scenario is defined by a vector x=[x i x j ] of Cartesian coordinates and wherein a square of a Euclidean distance between the unsafe actor and an autonomous vehicle E is minimized.

12. The method of claim 11 , wherein:
reducing the objective function with time based on a constraint on a derivative of γAE such that

∂

Assignees

• Nvidia Corp

Inventors

• Hari Siva Kumar Sastry
• Frosio Iuri
• Ghodsi Zahra
• Anandkumar Anima
• Tsai Timothy
• Keckler Stephen W
• Troccoli Alejandro

Classifications

• B60W2710/20

  Steering systems · CPC title

• B60W30/09

  Taking automatic action to avoid collision, e.g. braking and steering · CPC title

• B60W2720/125

  Lateral acceleration · CPC title

• B60W60/0011

  involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles · CPC title

• B60W2720/106

  Longitudinal acceleration · CPC title