24 citations · 47 across the 18 of their papers we have counts for
20 papers
Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment
Ziyao Wang, Maonan Wang, Yucheng He +5
Cloud removal (CR) is essential for optical remote sensing, serving as a prerequisite for reliable downstream interpretation, such as semantic segmentation and change detection. Ho…
OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
Maonan Wang, Zhengyan Huang, Kemou Jiang +13
Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics. Howev…
InfoGeo: Information-Theoretic Object-Centric Learning for Cross-View Generalizable UAV Geo-Localization
Hongyang Zhang, Maonan Wang, Ziyao Wang +2
Cross-view geo-localization (CVGL) is fundamental for precise localization and navigation in GPS-denied environments, aiming to match ground or UAV imagery with satellite views. Ex…
IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
Yuxin Cai, Zongtai Li, Maonan Wang +9
Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observability. Effective search rese…
ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control
Aoyu Pang, Maonan Wang, Yuejiao Xie +3
Reinforcement learning (RL) has shown promise in traffic signal control (TSC). However, its reliance on predefined states limits responsiveness to observable open-world events that…
Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency
Junming Liu, Yuqi Li, Yifei Sun +6
Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile. Models that answer an original input correctly can still fail under valid tr…
CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control
Xibei Chen, Yifeng Zhang, Yuxiang Xiao +3
Recent advances in robotics, automation, and artificial intelligence have enabled urban traffic systems to operate with increasing autonomy towards future smart cities, powered in…
Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach
Aoyu Pang, Maonan Wang, Zifan Sha +4
Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportati…
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…
Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach
Yuejiao Xie, Maonan Wang, Di Zhou +2
Urban Air Mobility (UAM) systems are rapidly emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportati…
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…
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows
Linjiang Cao, Maonan Wang, Xi Xiong
The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a classic NP-hard combinatorial optimization problem widely applied in logistics distribution and transportati…
CL-CoTNav: Closed-Loop Hierarchical Chain-of-Thought for Zero-Shot Object-Goal Navigation with Vision-Language Models
Yuxin Cai, Xiangkun He, Maonan Wang +3
Visual Object Goal Navigation (ObjectNav) requires a robot to locate a target object in an unseen environment using egocentric observations. However, decision-making policies often…
Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic
Yuting Wang, Lu Liu, Maonan Wang +1
The burgeoning field of autonomous driving necessitates the seamless integration of autonomous vehicles (AVs) with human-driven vehicles, calling for more predictable AV behavior a…
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement
Aoyu Pang, Maonan Wang, Man-On Pun +2
Urban congestion remains a critical challenge, with traffic signal control (TSC) emerging as a potent solution. TSC is often modeled as a Markov Decision Process problem and then s…
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…
A Multi-Agent Rollout Approach for Highway Bottleneck Decongestion in Mixed Autonomy
Lu Liu, Maonan Wang, Man-On Pun +1
The integration of autonomous vehicles (AVs) into the existing transportation infrastructure offers a promising solution to alleviate congestion and enhance mobility. This research…
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…