On the Comparability and Optimal Aggressiveness of the Adversarial Scenario-Based Safety Testing of Robots-Reference-Cited by-同舟云学术

On the Comparability and Optimal Aggressiveness of the Adversarial Scenario-Based Safety Testing of Robots

Published:2023-08 Issue:4 Volume:39 Page:3299-3318
ISSN:1552-3098
Container-title:IEEE Transactions on Robotics
language:
Short-container-title:IEEE Trans. Robot.

Author:

Weng Bowen¹^ORCID,Castillo Guillermo A.¹^ORCID,Zhang Wei²^ORCID,Hereid Ayonga³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Ohio State University, Columbus, OH, USA

2. Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

3. Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH, USA

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Science, Technology and Innovation Commission of Shenzhen Municipality

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/8860/10214173/10115509-aam.pdf

Reference54 articles.

1. Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies

2. Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

3. AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems

4. Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

5. General Lane-Changing Model MOBIL for Car-Following Models

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