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cs.CV2026
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…
cs.CV2026
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…
cs.CV2025
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…