Improving Car-Following Control in Mixed Traffic: A Deep Reinforcement Learning Framework with Aggregated Human-Driven Vehicles-Reference-Cited by-同舟云学术

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Improving Car-Following Control in Mixed Traffic: A Deep Reinforcement Learning Framework with Aggregated Human-Driven Vehicles

Published:2024-06-02 Issue: Volume:22 Page:627-632
ISSN:
Container-title:2024 IEEE Intelligent Vehicles Symposium (IV)
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Author:

Chen Xianda¹,Tiu PakHin¹,Zhang Yihuai¹,Zhu Meixin²,Zheng Xinhu²,Wang Yinhai³

Affiliation:

1. The Hong Kong University of Science and Technology (Guangzhou),Intelligent Transportation Thrust,Guangzhou,China,511400

2. Guangdong Provincial Key Lab of Integrated Communication,Sensing and Computation for Ubiquitous Internet of Things

3. University of Washington,Department of Civil and Environmental Engineering,Seattle,WA,USA,98195

Funder

National Natural Science Foundation of China

Guangzhou Municipal Science and Technology Project

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587320/10588370/10588405.pdf?arnumber=10588405

Reference38 articles.

1. Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

2. Human-like autonomous car-following model with deep reinforcement learning

3. An Architecture for Experiments in Connected and Automated Vehicles

4. A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon

5. Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

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