Socially Intelligent Reinforcement Learning for Optimal Automated Vehicle Control in Traffic Scenarios-Reference-Cited by-同舟云学术

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Socially Intelligent Reinforcement Learning for Optimal Automated Vehicle Control in Traffic Scenarios

Published:2024 Issue: Volume: Page:1-12
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Taghavifar Hamid¹^ORCID,Wei Chongfeng²^ORCID,Taghavifar Leyla³^ORCID

Affiliation:

1. Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada

2. School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, U.K.

3. School of Electrical and Electronics Engineering, Islamic Azad University (IAU), Tehran, Iran

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8856/4358066/10416251.pdf?arnumber=10416251

Cited by 2 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. A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain;Neural Computing and Applications;2024-09-04

2. Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making;Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering;2024-02-18

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