Sparse least squares support vector machine based methods for vehicle driving behavior recognition

Abstract

Intelligent vehicles are expected to accurately recognize the driving intentions of surrounding vehicles so as to precisely identify the hazards to automatic driving and accomplish reasonable motion planning. This paper introduces a model for vehicles driving behavior recognition (VDBR) based on Sparse Least Squares Support Vector Machine (S-LSSVM) by means of machine learning methods, with the subject vehicle and its surrounding vehicles as the research subject. First, the relative lateral displacement and relative lateral speed between vehicles are captured as the eigenvectors after calculation of trajectory curvature and change time window. Then, the pruning algorithm is used to make Least Squares Support Vector Machine (LSSVM) training samples sparse and the Particle Swarm Optimization algorithm (PSO) is employed to accomplish parameter tuning of S-LSSVM. Thus, a modified S-LSSVM model is constructed to grasp the interaction behavior between vehicles. The experiment results demonstrate that the S-LSSVM based model obtain better accuracy and timeliness compared with SVM and LSSVM on the Next Generation Simulation (NGSIM) dataset and the data from autonomous driving experimental platform.