Characteristics Analysis and Classification of Lane-changing Behavior after Following Process based on China-FOT

Abstract

Lane-changing is most common driving behavior, also an important research topic of autonomous driving. Based on China-FOT database, this paper extracts 237 lane-changing cases after following process, and TTC (Time to Collision) of the beginning moment of lane-changing behavior is selected as the parameter to analyze driver's behavior characteristics. Influence of six traffic environment factors on TTC is analyzed using K-S test and one-way ANOVA (Analysis of Variance). SVM (Support Vector Machine) is employed to categorize lane-changing cases after following process according to the gravity of the danger. The result shows that, distribution of TTC when driver ends following and begins changing lane can be fitted with lognormal curve, while concerning the normal cases, the differences between individuals are unobvious. Road type, lane changing direction, type of vehicle ahead and lighting condition are significant factors. Weather condition and velocity have no significant influence on the driver's lane-change behavior after following process, however, on average, driver changes lane obviously earlier in rainy weather. A linear boundary relating to velocity between normal lane changes and pre-crash cases is determined by IO-fold cross-validation SVM.