An Intelligent Smartphone-Based Road Accident Detection and Emergency Alert System Using Multi-Sensor Data Fusion and Machine Learning

Abstract

In the world, road traffic accidents are among the top causes of fatalities: There is a large risk of severe injuries and fatalities if an emergency response is late. This paper introduces an intelligent road accident detection and emergency alert system for a smartphone which is based on the multi-sensor data fusion and machine-learning techniques that allow the fast detection of the accident and early alert notification. The proposed framework uses the data from the vehicle's accelerometer, gyroscope and Global Positioning System (GPS) to continuously monitor the specific dynamics of the vehicle, recognizing the abnormal patterns of movement involved in road accidents. The sensor noise is eliminated in a preprocessing step, and discriminative motion features are extracted from the sensor signals, which are then classified by a Support Vector Machine (SVM) to discriminate between the collision and normal driving events and minimize false alarms. In the case of a potential accident being detected, the system activates a reprogrammable confirmation timer the user can use to cancel unintentional alerts before automatically sending the location of the accident as well as emergency information to preprogrammed contacts. The proposed method does not require any special in-vehicle hardware, and uses inexpensive sensors from existing smartphones, which are also widely available, so it is a cost-effective and readily deployable solution. The proposed framework is evaluated through experimentation, and the results show a high accuracy of detection with a low false-positive rate, while remaining real-time for practical implementation. Intelligent sensor fusion, machine learning-based classification, and automated emergency communication contribute to an enhanced road safety, minimizing emergency response time and improving the reliability of accident detections.