13 citations · 32 across the 21 of their papers we have counts for
26 papers
Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors
Fan Pu, Keshu Wu, Jiwan Jiang +5
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorr…
AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation
Keshu Wu, Hao Zhang, Rui Gan +4
Air-ground transportation research increasingly relies on co-simulation, yet constructing scenarios remains labor-intensive and difficult to validate. More importantly, a generated…
Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safety
Jingwen Zhu, Keshu Wu, Pei Li +3
Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide. Much of that harm concentrates at hotspo…
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
Kexin Tian, Sixu Li, Keshu Wu +2
Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics. Existing meth…
Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians
Keshu Wu, Sixu Li, Zihao Li +3
Traffic sensor data exhibit strong temporal and cross-sensor dependencies, yet long gaps erase both a sensor's local history and the joint observations needed to learn its relation…
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
Keshu Wu, Chenchen Kuai, Zihao Li +6
Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based…
A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges
Chenchen Kuai, Jiwan Jiang, Zihao Zhu +8
The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretraining data, distillation, and…
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
Xiangbo Gao, Yuheng Wu, Fengze Yang +7
While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substanti…
Automated Vehicles Should be Connected with Natural Language
Xiangbo Gao, Keshu Wu, Hao Zhang +3
Multi-agent collaborative driving promises improvements in traffic safety and efficiency through collective perception and decision making. However, existing communication media --…
Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen
Zihao Li, Xinyuan Cao, Xiangbo Gao +12
Traffic safety science has long been hindered by a fundamental data paradox: the crashes we most wish to prevent are precisely those events we rarely observe. Existing crash-freque…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics
Keshu Wu, Zihao Li, Sixu Li +3
This paper introduces an AI-enabled, interaction-aware active safety analysis framework that accounts for groupwise vehicle interactions. Specifically, the framework employs a bicy…
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
Hao Zhang, Ximin Yue, Kexin Tian +5
This paper presents a digital-twin platform for active safety analysis in mixed traffic environments. The platform is built using a multi-modal data-enabled traffic environment con…
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models
Rui Gan, Pei Li, Keke Long +4
Foundation models have demonstrated strong reasoning and generalization capabilities in driving-related tasks, including scene understanding, planning, and control. However, they s…
V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors
Keshu Wu, Pei Li, Yang Zhou +8
The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainab…
Human Mobility Reimagined: Digital Twin Intelligence for Adaptive Campus Course Timetabling
Keshu Wu, Xinyue Ye, Suphanut Jamonnak +1
Daily operations in large campuses depend on how efficiently people \emph{move} through space and time. In this sense, course timetables are more than administrative schedules: the…
Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments
Wenjing Gong, Xinyue Ye, Keshu Wu +4
Extreme heat events, exacerbated by climate change, pose significant challenges to urban resilience and planning. This study introduces a climate-responsive digital twin framework…
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
Junwei You, Rui Gan, Weizhe Tang +9
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those…
A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors
Keshu Wu, Pei Li, Yang Cheng +4
Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT…
Real-World Data Inspired Interactive Connected Traffic Scenario Generation
Junwei You, Pei Li, Yang Cheng +4
Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the…
Goal-based Neural Physics Vehicle Trajectory Prediction Model
Rui Gan, Haotian Shi, Pei Li +6
Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, th…
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
Keshu Wu, Yang Zhou, Haotian Shi +3
The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents consid…
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
Junwei You, Haotian Shi, Zhuoyu Jiang +6
Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving by leveraging information from bo…
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction
Junwei You, Haotian Shi, Keshu Wu +4
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditi…