The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 02, 2022

Filed:

Sep. 20, 2021
Applicant:

Motional Ad Llc, Boston, MA (US);

Inventors:

James Guo Ming Fu, Singapore, SG;

Scott D. Pendleton, Singapore, SG;

You Hong Eng, Singapore, SG;

Yu Pan, Singapore, SG;

Jiong Yang, Singapore, SG;

Assignee:

Motional AD LLC, Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 50/02 (2012.01); G06N 3/08 (2006.01); G06V 20/56 (2022.01); B60W 50/00 (2006.01);
U.S. Cl.
CPC ...
B60W 60/00186 (2020.02); B60W 50/0205 (2013.01); G06N 3/08 (2013.01); G06V 20/56 (2022.01); B60W 2050/0062 (2013.01); B60W 2554/80 (2020.02);
Abstract

Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.


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