Dynamic driving intention recognition of vehicles with different driving styles of surrounding vehicles

Abstract

Driving behaviour of surrounding vehicles have impacts on the security of ego vehicle. However, many vehicles do not use the turn signal in advance as a warning when changing lanes, which often leads to traffic accidents. Therefore, in the case of Internet of Vehicles (IoV) technology has not been popularized, recognizing the driving intention of surrounding vehicles timely and accurately through effective method is of great significance to ego vehicle. In this paper, a dynamic driving intention recognition method for surrounding vehicles based on adaptive multi-dimension continuous Gaussian mixture-HMM (AMCGM-HMM) is proposed. Considering that driving styles of surrounding vehicles are different, a feature-based multi-dimensional time series clustering algorithm is adopted to cluster the lane changing data samples and the results are used as the input of AMCGM-HMM. Parameters that may affect the recognition accuracy are optimized by multi-objective particle swarm optimization (MOPSO) algorithm to realize the adaptive of the method. The proposed method is verified on NGSIM dataset, and the application of this model is tested on the PreScan simulation platform combined with adaptive cruise control (ACC) system. Experimental results show that the proposed method can identify the driving intention of surrounding vehicles in advance and has high accuracy.