Understanding Real-World Traffic Safety through RoadSafe365 Benchmark
Xinyu Liu, Darryl C. Jacob, Yuxin Liu, Xinsong Du, Muchao Ye, Bolei Zhou, Pan He

TL;DR
RoadSafe365 is a large-scale, systematically organized vision-language benchmark designed to analyze real-world traffic safety, bridging official standards with data-driven understanding through extensive annotated video data and reasoning tasks.
Contribution
It introduces a comprehensive, hierarchical traffic safety benchmark with rich annotations and reasoning tasks, filling a gap in systematic evaluation aligned with safety standards.
Findings
Strong baseline performance established
Fine-tuning on RoadSafe365 improves accuracy
Cross-domain experiments validate effectiveness
Abstract
Although recent traffic benchmarks have advanced multimodal data analysis, they generally lack systematic evaluation aligned with official safety standards. To fill this gap, we introduce RoadSafe365, a large-scale vision-language benchmark that supports fine-grained analysis of traffic safety from extensive and diverse real-world video data collections. Unlike prior works that focus primarily on coarse accident identification, RoadSafe365 is independently curated and systematically organized using a hierarchical taxonomy that refines and extends foundational definitions of crash, incident, and violation to bridge official traffic safety standards with data-driven traffic understanding systems. RoadSafe365 provides rich attribute annotations across diverse traffic event types, environmental contexts, and interaction scenarios, yielding 36,196 annotated clips from both dashcam and…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic and Road Safety · Adversarial Robustness in Machine Learning
