4 papers · 1 filter
TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding
Yuqiang Lin, Yan Shi, Sam Lockyer +5
Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To address this challenge, we propos…
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…
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…
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…