Methodology and mobile application for driver behavior analysis and accident prevention


This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal smartphone. For the driver behavior monitoring, the smartphone’s cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following driver’s online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.

IEEE transactions on intelligent transportation systems, 21(6)
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Igor Lashkov
Igor Lashkov
Postdoctoral Scholar

My research interests include human-centered applications of machine learning, advanced driver assistance systems, and connected vehicle applications.