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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Vydané v:	Expert systems with applications Ročník 268; s. 126319
Hlavní autori:	Wang, Shufeng, Wang, Zhengli, Wang, Xinkai, Liang, Qingwei, Meng, Lingyi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 05.04.2025
Predmet:	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
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