24 citations · 47 across the 18 of their papers we have counts for
6 papers · 1 filter
TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making
Maonan Wang, Yirong Chen, Yuxin Cai +12
Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on p…
VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture
Maonan Wang, Yirong Chen, Aoyu Pang +4
Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods - ranging from rule-based heurist…
Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
Maonan Wang, Yirong Chen, Yuheng Kan +4
Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal co…
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Maonan Wang, Aoyu Pang, Yuheng Kan +3
Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion mana…
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
Maonan Wang, Xi Xiong, Yuheng Kan +2
Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems. While existing Reinforcement Learning…
An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks
Xi Xiong, Maonan Wang, Dengfeng Sun +1
Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume t…