Driving behaviour recognition based on orientation and position deviations Online publication date: Sat, 15-Jun-2019
by Wei Sun; Xiaorui Zhang; Xu Zhang; Xiaozheng He; Guoce Zhang
International Journal of Sensor Networks (IJSNET), Vol. 30, No. 3, 2019
Abstract: This paper proposes a driving behaviour recognition method, which applies vehicle orientation and position deviations to warn the driver against possible dangers. We integrate a gradient reinforcement method based on the linear discriminate analysis (LDA) to reinforce lane edges. An improved Canny operator based on adaptive threshold segmentation is exploited to extract the lane edges reliably. Based on an improved Hough transform algorithm, the reinforced lane edges help the detection of polar angle and polar radius of lanes that are used to calculate the vanishing point position. After that, the proposed method predicts current-frame lane parameters based on the previous-frame parameters through using the Kalman filter. Combining deviation angle and deviation distance, the proposed method categorises vehicle lane-keeping behaviour into three states: normal, left deviation, and right deviation. Experimental results of a variety of travelling scenes show that the proposed method outperforms other existing methods in precision.
Online publication date: Sat, 15-Jun-2019
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