Post by Vashu Chauhan

Globally, road traffic accidents result in over 1.19 million deaths each year, with many fatalities preventable through timely medical care. Delays in emergency medical services (EMS) are associated with a 46% increase in mortality from motor vehicle crashes. Beyond the human cost, traffic accidents also lead to slower speeds, longer travel times, and increased congestion - where reducing incident duration can save approximately $65 per minute per incident. We introduce IMPACT: Integrated Multimodal Pipeline for Rapid Accident Tracking, presented at the AAAI 2026 Student Abstract Track in Singapore. IMPACT is a scalable AI framework for real-time traffic incident understanding, combining efficient computer vision with multimodal reasoning. Our approach features a low-latency key-frame detection algorithm (~24 FPS on an Intel Core i5 CPU), implemented in C++. Key highlights from our study: 1. 92%+ reduction in MLLM calls compared to naive frame-skipping approaches, enabling practical large-scale deployment 2. A hybrid pipeline combining classical vision techniques (optical flow, keypoint tracking) with multimodal reasoning It was a great experience presenting this work and engaging with the community on building efficient, scalable, and interpretable multimodal systems for safety-critical applications. The codebase is available in both Python and C++. Code & Dataset: [https://lnkd.in/ghQuMG-h] Paper : [https://lnkd.in/gZ7ZtddN] More related things are on the way !! A huge thanks to my co-authors and supervisors Manisha Luthra, Carsten Binnig, Dr. Rajiv Ratn SHAH, Uelison Jean Lopes dos Santos #AAAI #AAAI2026 #MultimodalAI #ComputerVision #MLLM #GenerativeAI #AIResearch #TrafficSafety #SmartCities #AutonomousSystems #DeepLearning #EdgeAI #RealTimeAI