Driving Style Recognition Based on Lane Change Behavior Analysis Using Naturalistic Driving Data

Based on the driver.s driving habits and behavior in driving, the driver.s driving style can be divided into aggressive or normal. Driving style has a significant impact on driving safety, road traffic efficiency and vehicle energy consumption, and so on. Accurate driving style evaluation is essential to improve driving safety and reduce energy consumption. This study proposes a driver driving style evaluation model based on lane change behavior using clustering algorithm. The study extracted 2,861 lane change segments of 16 drivers from naturalistic driving data and the lane change behaviors were analyzed under the "comparable environment" in which the external traffic environment (including road facility type, traffic congestion, weather conditions, etc.) is basically the same. This study also discusses the sample size required for feature extraction. The research shows that different types of drivers have significant differences in the duration of the lane change, forward acceleration, lateral acceleration, and TTC.