Recognition method for abnormal driving behaviors of road traffic based on lane-level positioning

In order to improve the recognition accuracy of abnormal driving behavior recognition methods, a recognition method for abnormal driving behaviors of road traffic based on lane-level positioning is proposed. Firstly, a Gabor filter is used to extract the texture features of images.By transforming the images into linear digital signals which are not two-dimensional discrete, the gradient features in the images are obtained based on differences. Then, the lane-level positioning method is used to fuse the basic features of the extracted driving behavior images and detect the road boundary information in the images. Finally, the typical abnormal driving behaviors in the feature vector are fused, and the relationship between the typical abnormal driving behaviors is predicted by matching, testing and classifying the test samples, so as to complete the recognition and judgement of the driver's abnormal driving behaviors. In the experimental demonstration, the recognition accuracy of the proposed method is verified by comparative experiments, and the results show that the recognition accuracy of the proposed method is higher than 80% in the classification of different abnormal driving behaviors, which is much higher than that of the contrast methods. It shows that this method proposed in this paper can effectively improve the recognition accuracy of abnormal driving behaviors.