ADAS-RL: Safety learning approach for stable autonomous driving

Stability is the most significant component of an autonomous driving system, affecting both the lives of drivers and pedestrians and traffic flow. Reinforcement learning (RL) is a representative technology used in autonomous driving, but it has challenges because it is based on trial and error. In t...

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Vydané v:	ICT express Ročník 8; číslo 3; s. 479 - 483
Hlavní autori:	Lee, Dongsu, Kwon, Minhae
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier 01.09.2022
Predmet:	Autonomous vehicle Deep reinforcement learning algorithm Markov decision process
ISSN:	2405-9595, 2405-9595
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Popis
Shrnutí:	Stability is the most significant component of an autonomous driving system, affecting both the lives of drivers and pedestrians and traffic flow. Reinforcement learning (RL) is a representative technology used in autonomous driving, but it has challenges because it is based on trial and error. In this letter, we propose an efficient learning approach for stable autonomous driving. The proposed deep reinforcement learning based approach can address the partially observable scenario in mixed traffic which includes both autonomous vehicles and human-driven vehicles. Simulation results show that the proposed model outperforms the control-theoretic and vanilla RL approaches. Furthermore, we confirm the effect of the sync-penalty, which teaches the agent about unsafe decisions without experiencing the accidents.
ISSN:	2405-9595 2405-9595
DOI:	10.1016/j.icte.2022.05.004