Evidence-based automated traffic hazard zone mapping using wearable sensors

Recently, underestimating traffic condition risk is considered one of the biggest reasons for traffic accidents. In this paper, we proposed evidence-based automatic hazard zone mapping method using wearable sensors. Here, we measure driver's behavior using three-axis gyro sensors. Analyzing the measured motion data, proposed method can label characteristic motion that is observed at hazard zone. We gathered motion data sets form two types of driver, i.e., an instructor of driving school and an ordinary driver, then, tried to generate traffic hazard zone map focused on difference of the motions. Through the experiment in public road, we confirmed our method allows to extract hazard zone.