A new dataset of diverse traffic accidents.
18 PAPERS • 1 BENCHMARK
The Traffic Accident Prediction (TAP) data repository offers extensive coverage for 1,000 US cities (TAP-city) and 49 states (TAP-state), providing real-world road structure data that can be easily used for graph-based machine learning methods such as Graph Neural Networks. Additionally, it features multi-dimensional geospatial attributes, including angular and directional features, that are useful for analyzing transportation networks. The TAP repository has the potential to benefit the research community in various applications, including traffic crash prediction, road safety analysis, and traffic crash mitigation. The datasets can be accessed in the TAP-city and TAP-state directories.
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CAP-DATA is a large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames together with labeled fact-effect-reason-introspection description and temporal accident frame label. It can support many useful tasks for accident inference, such as accident detection and prediction (AccidentDet/Pre), causal inference of accident (Accident-Causal), accident classification (Accident-Cla), text-video based accident retrieval (Accident-Retri), and question answering in an accident (Accident-QA) of the driving scene.
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