TUN-DAS: Time-Series Analysis and Unsupervised Learning Based Driving Behavior Assessment System

Traffic accidents, mostly caused by human errors, pose a significant challenge in automotive safety. This paper introduces TUN-DAS, a novel Time-series analysis and Unsupervised learning-based Driving behavior Assessment System, designed to systematically evaluate drivers’ behavior and identify potential risks. Unlike conventional in-cabin driver monitoring systems, which generally rely on rule-based or supervised methods, TUN-DAS utilizes a unique time-series clustering approach. This method combines a k-means algorithm with Dynamic Time Warping (DTW) and DTW Barycenter Averaging (DBA), enabling a more nuanced and comprehensive assessment of driving behavior. The modification of conventional DTW in our system allows for effective computation of temporal differences between driving data. TUN-DAS stands out in its ability to detect both sporadic human errors and consistent unsafe driving patterns by analyzing anomalies within individual drivers and across a broader driver dataset. Our validation with bus driver data shows TUN-DAS’s effectiveness in driving behavior assessment, significantly achieving better accuracy than traditional rule-based evaluation methods and allowing for quantitative and temporal analysis of a series of driving behavior. This system holds promise for enhancing road safety by facilitating the early identification of high-risk drivers, thereby contributing to the development of safer driving environments.