Continuous Decision-Making in Lane Changing and Overtaking Maneuvers for Unmanned Vehicles: A Risk-Aware Reinforcement Learning Approach With Task Decomposition-Reference-Cited by-同舟云学术

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Continuous Decision-Making in Lane Changing and Overtaking Maneuvers for Unmanned Vehicles: A Risk-Aware Reinforcement Learning Approach With Task Decomposition

Published:2024-04 Issue:4 Volume:9 Page:4657-4674
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Wu Sifan¹^ORCID,Tian Daxin¹^ORCID,Duan Xuting¹^ORCID,Zhou Jianshan¹^ORCID,Zhao Dezong²^ORCID,Cao Dongpu³^ORCID

Affiliation:

1. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing, China

2. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

3. School of Vehicle and Mobility, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7274857/10555388/10477452.pdf?arnumber=10477452

Reference60 articles.

1. Joint Optimization of Sensing, Decision-Making and Motion-Controlling for Autonomous Vehicles: A Deep Reinforcement Learning Approach

2. An automated driving systems data acquisition and analytics platform

3. Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles

4. Near Crashes as Crash Surrogate for Naturalistic Driving Studies

5. Secure Cooperative Localization for Connected Automated Vehicles Based on Consensus

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