Signal-Based Crash Detection Using Accelerometer Data

One of the most significant international issues is road traffic accidents, which tend to result in serious trauma among individuals and slow reaction to emergency. This paper will present a signal-based crash detection system which makes use of accelerator data to facilitate the detection of a car crash in real time. The method uses tri-axial measurements of acceleration to record sudden transitions of the motion, which signifies collision events. The reading of accelerators is used to calculate a Signal Magnitude Vector (SMV) and then it is preprocessed using noise filtering, and gravity compensations to improve signal quality. The system uses both machine learning-based and threshold-based models to differentiate between the normal and crash situation driving patterns. The evaluation is done experimentally with a mixture of simulated and real life driving data and the performance is quantified in terms of accuracy, precision, recall and detection latency. The suggested approach has an excellent detection rate and low false alarms due to sudden braking or road anomalies. Also, the system facilitates real-time alert generation through incorporation of location-based services to send an emergency notification. The findings indicate that a low-cost, scalable, and efficient crash detection system in the intelligent transportation system and mobile-based safety applications is possible.