Failure-Scenario Maker for Autonomous Driving Vehicle using Adversarial Multi-agent Reinforcement Learning
The author proposes a method to create failure-scenarios of an autonomous driving vehicle by training other surrounding vehicles by means of multiagent adversarial reinforcement learning. Failure in driving environments might lead to catastrophic results. Hence, when developing a software of autonomous driving cars, one must find as many failure-cases as possible and then improve the software. However, as the software becomes complicated, it is hard to find failure-scenarios that are useful for the software improvement. Hence, the author proposes a framework to create various failure-scenarios of an autonomous car by training other car(s) via reinforcement learning. He demonstrates the effectiveness of his proposed method with two kinds of experiments.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
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Authors:
- Wachi, Akifumi
- Publication Date: 2020-9
Language
- English
- Japanese
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: pp 950-955
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Serial:
- Transactions of Society of Automotive Engineers of Japan
- Volume: 51
- Issue Number: 5
- Publisher: Society of Automotive Engineers of Japan
- ISSN: 0287-8321
- EISSN: 1883-0811
- Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Crash avoidance systems; Driving simulators; Fail safe systems; Machine learning; Multi-agent systems; Software; Vehicle safety
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01756456
- Record Type: Publication
- Source Agency: Japan Science and Technology Agency (JST)
- Files: TRIS, JSTAGE
- Created Date: Oct 29 2020 9:25AM