Driver Intention identification based on Vehicle Driving State and Driving Behavior Interaction

Open Access
Conference Proceedings
Authors: Zehong WeiLan ZhaoYuanyuan RenRan YangXuelian ZhengZhimao Hu

Abstract: An intention identification method is proposed in this paper, which is based on vehicle running state and driving behavior interaction. The driving simulator experiment is designed to obtain vehicle driving time series data of lane keeping and lane changing. Based on the stage division of lane changing process, the lane changing intention stage can be obtained. It is found that the length of lane changing intention stage approximately obeys the normal distribution, and the length of left lane changing intention stage is greater than that of right lane changing. When exploring the influencing factors on the length of lane changing intention stage, it is found that the length of intention stage is approximately linearly correlated with the average speed of vehicles. Finally, based on the vehicle running state and intention stage, a continuous hidden Markov model based on Gaussian probability density function is established, which can effectively identify the driver's lane changing intention with high recognition accuracy and great real-time performance. The average accuracy of the model is 90%, the advanced time of left and right lane changing intention is 1.5s and 1.4s.

Keywords: driving behavior, vehicle running state, driver intention stage, lane changing intention identification, GMM-CHMM

DOI: 10.54941/ahfe1002466

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