Invention Grant
- Patent Title: System and method for full-stack verification of autonomous agents
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Application No.: US15955320Application Date: 2018-04-17
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Publication No.: US10671077B2Publication Date: 2020-06-02
- Inventor: German Ros Sanchez
- Applicant: TOYOTA RESEARCH INSTITUTE, INC.
- Applicant Address: US CA Los Altos
- Assignee: TOYOTA RESEARCH INSTITUTE, INC.
- Current Assignee: TOYOTA RESEARCH INSTITUTE, INC.
- Current Assignee Address: US CA Los Altos
- Agency: Seyfarth Shaw LLP
- Main IPC: G05D1/02
- IPC: G05D1/02 ; G05D1/00 ; G06N3/08

Abstract:
A method for full-stack verification of autonomous agents includes training a neural network to learn a noise model associated with an object detection module of an autonomous agent system of an autonomous vehicle. The method also includes replacing the object detection module of the autonomous agent system with the neural network and a sensory input of the object detection module with ground truth information to apply a surrogate function to the ground truth information. The method further includes verifying the autonomous agent system including the trained neural network to apply the surrogate function in response to the ground truth information to simulate sensor information data to at least a planner module of the autonomous agent system. The method also includes controlling a behavior of the autonomous vehicle using the verified autonomous agent system including the object detection module.
Public/Granted literature
- US20190317510A1 SYSTEM AND METHOD FOR FULL-STACK VERIFICATION OF AUTONOMOUS AGENTS Public/Granted day:2019-10-17
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