Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections-Reference-Cited by-同舟云学术

Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

Published:2022-09 Issue:9 Volume:23 Page:15850-15863
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Bai Zhengwei¹^ORCID,Hao Peng²^ORCID,ShangGuan Wei³^ORCID,Cai Baigen³^ORCID,Barth Matthew J.¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA

2. Center for Environmental Research and Technology, University of California at Riverside, Riverside, CA, USA

3. State Key Laboratory of Rail Traffic Control and Safety and the Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

Funder

National Center for Sustainable Transportation

USDOT through the University Transportation Centers (UTC) Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9893028/09700479.pdf?arnumber=9700479

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4. A hybrid approach to estimating electric vehicle energy consumption for ecodriving applications

5. Congested traffic states in empirical observations and microscopic simulations

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