Intelligent vehicle driving decision-making model based on variational AutoEncoder network and deep reinforcement learning

In this paper, an end-to-end driving decision-making model is proposed for intelligent vehicle, utilizing a Variational AutoEncoder (VAE) network and Deep Reinforcement Learning to address the challenges in complex and dynamical driving environments. Firstly, the traffic environment image features a...

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Veröffentlicht in:	Expert systems with applications Jg. 268; S. 126319
Hauptverfasser:	Wang, Shufeng, Wang, Zhengli, Wang, Xinkai, Liang, Qingwei, Meng, Lingyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 05.04.2025
Schlagworte:	AutoEncoder Network Driving Decision-making Driving risk field Intelligent Vehicle Rule constraints Soft Actor-Critic algorithm Rule constraints Soft Actor-Critic algorithm AutoEncoder Network Intelligent Vehicle Driving Decision-making Driving risk field
ISSN:	0957-4174
Online-Zugang:	Volltext
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