A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism-Reference-Cited by-同舟云学术

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A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism

Published:2024-03-31 Issue: Volume: Page:1-17
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Liu Chunyu¹²,Sheng Zihao²,Li Pei²,Chen Sikai²,Luo Xia³,Ran Bin²

Affiliation:

1. Sichuan Shudao New System Rail Group Co. Ltd, Chengdu, Sichuan, China

2. Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA

3. Department School of Transportation and Logistics, National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2024.2335084

Reference50 articles.

1. Model Predictive Control of Transitional Maneuvers for Adaptive Cruise Control Vehicles

2. Dynamical model of traffic congestion and numerical simulation

3. Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

4. A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction;Computer-Aided Civil and Infrastructure Engineering;2024-07-07

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