Driving Risk Assessment using Cluster Analysis based on Naturalistic Driving Data

In addition to the real traffic accident data, naturalistic driving data can allow researchers gain insights into the factors that cause risk/hazard situations. This paper considers a comprehensive naturalistic driving experiment to collect detailed driving data on actual Chinese roads. Using acquired real-world driving data, a near-crash database is built, which contains vehicle status, potential crash object, driving environment and road type, and weather condition. K-means cluster analysis is applied to classify the near-crash cases into different driving risk levels using braking process features, namely maximum deceleration, average deceleration and percentage reduction in the vehicle kinetic energy. The results indicate that the velocity when braking and triggering factors have strong relationship with the driving risk level involved in near-crash cases.