Predicting Risks Before They Happen: Vision-Language Reasoning for Autonomous Driving
Overview
This paper introduces PotentialRiskQA, an ego-view driving dataset designed to link observable precursors to potential risks and mitigation actions. The authors also present PR-Reasoner, a vision-language model trained on this dataset that generates precursor-centered reasoning chains. Experiments demonstrate improved scene understanding, risk cognition, and action quality with fewer unsupported risk claims compared to existing methods.
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- arXiv Robotics — research abstractsSignalPredicting Risks Before They Happen: Vision-Language Reasoning for Autonomous Driving
This paper introduces PotentialRiskQA, an ego-view driving dataset designed to link observable precursors to potential risks and mitigation actions. The authors also present PR-Reasoner, a vision-language model trained on this dataset that generates precursor-centered reasoning chains. Experiments demonstrate improved scene understanding, risk cognition, and action quality with fewer unsupported risk claims compared to existing methods.
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