Large-Scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing
Author:
Affiliation:
1. Department of Computer Science and TransGP, The University of Hong Kong, Pokfulam, Hong Kong
2. Min H. Kao Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
Funder
NVIDIA
University of Tennessee, Knoxville, TN, USA
Innovation and Technology Commission of the HKSAR Government under the InnoHK Initiative
NSF
ITF GHP/126/21GD
HKU’s CRF Seed Grant
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
https://ieeexplore.ieee.org/ielam/6488907/10384482/10324334-aam.pdf
Reference29 articles.
1. Flow: A Modular Learning Framework for Mixed Autonomy Traffic
2. Reinforcement Learning for Mixed Autonomy Intersections
3. Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections;Wang;arXiv:2301.05294,2023
4. Mixed traffic control and coordination from pixels;Villarreal;arXiv:2302.09167,2023
5. White Phase Intersection Control Through Distributed Coordination: A Mobile Controller Paradigm in a Mixed Traffic Stream
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2. Mixed Traffic Control and Coordination from Pixels;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
3. Guest Editorial Special Issue on Recent Advances of Security, Privacy, and Trust in Mobile Crowdsourcing;IEEE Internet of Things Journal;2024-01-15
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