TY  - JOUR
PY  - 2020//
TI  - Failure-scenario maker for autonomous driving vehicle using adversarial multi-agent reinforcement learning
JO  - Transactions of Society of Automotive Engineers of Japan
A1  - Wachi, Akifumi
SP  - 950
EP  - 955
VL  - 51
IS  - 5
N2  - 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>
LA  - ja
SN  - 0287-8321
UR  - http://dx.doi.org/10.11351/jsaeronbun.51.950
ID  - ref1
ER  -