Affiliations: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
Correspondence:
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Corresponding author. Kai Ren, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China. E-mail: renkai@zzu.edu.cn.
Abstract: In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.
Keywords: Lane departure warning, steerable filters, Kalman filtering fuzzy logic
algorithm, vision and hearing
