IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)
Vashu Chauhan, Avinash Anand, Manisha Luthra, Uelison Jean Lopes dos Santos, Carsten Binnig, Rajiv Ratn Shah
Abstract
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 real-time 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.
BibTeX
@inproceedings{aaai2026_impactintegrated,
title = {IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)},
author = {Vashu Chauhan and Avinash Anand and Manisha Luthra and Uelison Jean Lopes dos Santos and Carsten Binnig and Rajiv Ratn Shah},
booktitle = {AAAI 2026},
year = {2026}
}