Model-free Deep Reinforcement Learning for Urban Autonomous Driving

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in suboptimal solutions and is expensive to develop, generalize and maintain at scal...

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Veröffentlicht in:	2019 IEEE Intelligent Transportation Systems Conference (ITSC) S. 2765 - 2771
Hauptverfasser:	Chen, Jianyu, Yuan, Bodi, Tomizuka, Masayoshi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2019
Schlagworte:	Autonomous vehicles Decision making Learning (artificial intelligence) Machine learning Roads Routing Task analysis
Online-Zugang:	Volltext
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