EAI-EMCA: An Edge-AI–Enabled Multisensory Framework for Intelligent Collision Recognition and Smart Vehicular Incident Response Architecture

Early notifications about vehicular crashes and emergent dissemination of emergency messages are essential in ensuring that deaths caused by accidents are minimized but the current accident detections systems are prone to high latency, false alarms, and low levels of deploy ability owing to dependency on cloud services and computationally expensive models. In this paper, the author suggests a proposal of edgeintelligent crash detection and emergency alert system, which is fully based on resource constrained vehicular boards. The proposed EAI-EMCA model combines an edge-smart inference module to ensure higher reliability in crashes and lower response timing by an ultra-low paradigm. The suggested framework makes use of closely coordinated multisensory fusion of inertial and motion signals and a small learning model that is optimized to deterministic inference time. The system is capable of real-time crash classification and automated alert generation at the edge, in contrast to threshold-based or cloudassisted, which reduce the time to respond to an event significantly. Wide experiments performed with the growing amount of data, prove the effectiveness and scalability of the suggested method. The intelligent module embedded in it is built to conduct real-time severity diagnosis and confidence testing to minimize false alarms and enhance the soundness of the decisions in emergency response. The system runs with maximized detection accuracy of 96.8% and inference latency is limited to less than 23 ms at an end to alert time of less than 98 ms. It also represents a false alarm rate of 2.5% and small model size of 14.7 MB which proves that it can fit in the real world to be used in a vehicle. These findings confirm that the given system is useful in balancing the accuracy, responsiveness, and edge feasibility, further developing feasible intelligent transportation safety solutions.