A Data-Driven Approach to Vehicle Safety: IoT and ML Accident Detection

Accidents prevent injured people from communicating in emergencies. They may use IoT to send accident data to an emergency unit as soon as possible, depending on severity. IoT networks use standard models to categorize situation severity and report it to the control unit. These obsolete models operate poorly. Logistic regression was presented to overcome embedded, cloud, and server model restrictions. The suggested approach is applied to a previous accident database and provided to the local emergency unit for quick testing. Unlike a human review, the suggested technology instantly discovers accident data and delivers it to the local cloud database. A nearby guardian receives accident data from the user's Sensors. IoT networking & emergency alerting to local guardians benefit from SVM with Logistic regression (LR). The suggested application got 96.74% accuracy, 98.67% sensitivity, 96.86% recall, 96.54% throughput, and 95.87% F1 score, which was good.