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Publikation

IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)

Vashu Chauhan; Avinash Anand; Manisha Luthra; Uélison Santos; Carsten Binnig; Rajiv Ratn Shah
In: Sven Koenig; Chad Jenkins; Matthew E. Taylor (Hrsg.). Fortieth AAAI Conference on Artificial Intelligence, Thirty-Eighth Conference on Innovative Applications of Artificial Intelligence, Sixteenth Symposium on Educational Advances in Artificial Intelligence, AAAI 2026, Singapore, January 20-27, 2026. AAAI Conference on Artificial Intelligence (AAAI), Pages 41159-41161, AAAI Press, 2026.

Zusammenfassung

Traffic accidents pose a significant societal challenge, with many fatalities being avoidable through timely emergency response. We introduce IMPACT (Integrated Multimodal Pipeline for Rapid Accident Causality Tracking), a scalable AI framework designed for autonomous, rapid traffic incident analysis using existing urban CCTV infrastructure. IMPACT combines a low-latency CPU-based vision module for realtime key-frame filtering (24 FPS) with the causal reasoning capabilities of MLLMs, reducing costly MLLM calls by over 92% compared to naive sparse sampling. We further present TRACE10K, a dataset featuring three-tier textual annotations that describe accident dynamics at the frame-sequence level.

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