MULTI-MODAL DEEP LEARNING FRAMEWORK FOR REAL-TIME VEHICLE ACCIDENT DETECTION AND EMERGENCY RESPONSE USING EMBEDDED COMPUTER VISION AND AUDIO PROCESSING

Abstract

Car crashes have been one of the main causes of death and financial loss in any part of the world, and this is the reason why the world has to use the best automated systems that will enable quick response to the emergency. The present paper introduces a new multi-modal accident detection framework that combines the visual and audio processing functionality using the deep learn- ing architectures adapted to the real-time embedded operational mode. It uses a Raspberry Pi 4 with a hybrid 3D Convolutional Neural Network-Long Short Term Memory (3D CNN-LSTM) visual processing model and a Raspberry Pi 4 with a Recurrent Neural Network with Gated Recurrent Units (RNN-GRU) audio processing machine as its system. Multi-modal fusion framework It is a framework that incorporates both levels of prediction to produce a final output by a learned fusion network (using dynamic confidence thresholding). Training was performed on the large-scale dataset of 47,500 video sequences and 62,800 audio clips obtained based on the crash test databases and the real world incident recordings. The system shows exceptional performance of 96.8and 97.2approaches currently. The average latency of real-time infer- ence is 71.5ms, which is comparable to the automotive safety requirements. The system was evaluated within a controlled simulation environment that in- cluded a wide range of virtual conditions. The results indicated strong practical effectiveness with high levels of accuracy. Its integrated response mechanism demonstrated a notable reduction in reaction time, supported by automated no- tification features. Statistical analysis showed clear improvements over base- line methods, with substantial effect sizes and stable confidence intervals. Re- liability assessments indicated consistently high operational availability and long intervals between failures, reflecting dependable system performance.