6 papers
TAU-R1: Visual Language Model for Traffic Anomaly Understanding
Yuqiang Lin, Kehua Chen, Sam Lockyer +12
Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in vi…
Physics-Embedded Gaussian Process for Traffic State Estimation
Yanlin Chen, Kehua Chen, Yinhai Wang
Traffic state estimation (TSE) becomes challenging when probe-vehicle penetration is low and observations are spatially sparse. Pure data-driven methods lack physical explanations…
Diffusion^2: Dual Diffusion Model with Uncertainty-Aware Adaptive Noise for Momentary Trajectory Prediction
Yuhao Luo, Yuang Zhang, Kehua Chen +4
Accurate pedestrian trajectory prediction is crucial for ensuring safety and efficiency in autonomous driving and human-robot interaction scenarios. Earlier studies primarily utili…
Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction
Bingzhang Wang, Kehua Chen, Yinhai Wang
Trajectory prediction is a fundamental task in Autonomous Vehicles (AVs) and Intelligent Transportation Systems (ITS), supporting efficient motion planning and real-time traffic sa…
From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety
Shucheng Zhang, Yan Shi, Bingzhang Wang +6
Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical challenge, as conventional infrastructure-based measures are often insuffi…
Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersections
Kehua Chen, Shucheng Zhang, Ryan Feng Lin +1
Modeling vehicle interactions at unsignalized intersections is a challenging task due to the complexity of the underlying game-theoretic processes. Although prior studies have atte…