Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

Road traffic accidents are caused 1.25 million deaths per year worldwide. To improve road safety and reducing road accidents, a recognition method for driving events is introduced in this paper. The proposed method detected and classified both driving behaviors and road anomalies patterns based on smartphone sensors (accelerometer and gyroscope). k-Nearest Neighbor and Dynamic Time Warping algorithms were utilized for method evaluation. Experiments were conducted to evaluate k-nearest neighbor and dynamic time warping algorithms accuracy for road anomalies and driving behaviors detection, moreover, driving behaviors classification. Evaluation results showed that k-nearest neighbor algorithm detected road anomalies and driving behaviors with total accuracy 98.67%. Dynamic time warping algorithm classified (normal and abnormal) driving behaviors with total accuracy 96.75%.