Deep Reinforcement Learning-Based Lane-Changing Trajectory Planning for Connected and Automated Vehicles-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Lane-Changing Trajectory Planning for Connected and Automated Vehicles

Published:2023-11-17 Issue: Volume:2 Page:406-412
ISSN:
Container-title:2023 9th International Conference on Mechanical and Electronics Engineering (ICMEE)
language:
Short-container-title:

Author:

Liu Jianbei¹,Feng Yao²,Jing Shoucai³,Hui Fei⁴

Affiliation:

1. Tongji University,The Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Shanghai,China

2. Chang'an University,School of Information Engineering,Xi'an,Shaanxi,China

3. CCCC First Highway Consultants Co., Ltd, School of Information Engineering, Chang'an University,Xi'an,Shaanxi,China

4. CCCC First Highway Consultants Co. Ltd.,Xi'an,Shaanxi,China

Funder

National Key R&D Program of China

Key Research and Development Program of Shanxi Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10525258/10525262/10525509.pdf?arnumber=10525509

Reference16 articles.

1. Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

2. Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

3. AGV path planning and obstacle avoidance using Dijkstras algorithm [J];Shaikh;Intl J Appl Innov Engi Manag,2013

4. Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

5. Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system

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