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Living in a world of high-density traffic leads to numerous issues, including significant loss of life due to ambulance delay caused by heavy congestion. To overcome this problem, we must automate the process for clearing traffic junctions as Ambulances arrive at these points and ensure they reach their destination on time. Our main objective is preserving human lives while lowering mortality rates created by tardiness. The implementation of Deep Learning coupled with embedded systems play a critical role here. Audio detectors capture siren sounds. CNN (Convolutional Neural Network) and ANN (Artificial Neural Network) algorithms detect the ambulance in real time from the live footage and classify the sound produced when responding emergency vehicles move through intersections effectively using YOLO (You Only Look Once) algorithm. Then, Raspberry Pi is programmed to integrate input data and the model required towards modifying our still functioning Traffic algorithm hence achieving more precise results. Hopefully, providing assistance from above-described technologies will eventually result in reduced death counts and arising out delayed medical interventions.
Gopinathan et al. (Wed,) studied this question.