@article{ref1,
title="Failure-scenario maker for autonomous driving vehicle using adversarial multi-agent reinforcement learning",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2020",
author="Wachi, Akifumi",
volume="51",
number="5",
pages="950-955",
abstract="We propose 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, we 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, we propose a framework to create various failure-scenarios of an autonomous car by training other car(s) via reinforcement learning. We demonstrate the effectiveness of our proposed method with two kinds of experiments.<p /><p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.51.950",
url="http://dx.doi.org/10.11351/jsaeronbun.51.950"
}