Enhancing Autonomous Driving: A Scalable Multi-Layered DRL Approach for Decision and Control-Reference-Cited by-同舟云学术

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Enhancing Autonomous Driving: A Scalable Multi-Layered DRL Approach for Decision and Control

Published:2024-05-17 Issue: Volume: Page:1685-1690
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Wu Liangguang¹,Zhou Kailong¹,Fei Ting¹,Yao Maojia²,Han Hao¹,Li Honglin¹

Affiliation:

1. Dongfeng Motor Corporation Research and Development Institute,Wuhan,P. R. China,432000

2. School of Automotive Engineering, Wuhan University of Technology,Wuhan,P. R. China,430070

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606682.pdf?arnumber=10606682

Reference7 articles.

1. Deep Reinforcement Learning for Autonomous Driving: A Survey

2. Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning

3. Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey

4. Autonomous Motion Decision-making based on Deep Reinforcement Learning for Autonomous Driving

5. Conditional DQN-Based Motion Planning With Fuzzy Logic for Autonomous Driving

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