基于视觉的大半径圆曲线车道线识别

车道线识别是自动驾驶中车道保持系统的关键技术，为提高大半径圆曲线车道线识别精度，本文将两侧弯曲车道线化分为多区域，利用直线段和贝塞尔曲线进行拟合。首先在感兴趣区域(ROI)进行图像预处理，并根据车道线特征像素点的分布情况，通过车道线变化规律判定转向；然后根据转向按照不同的比例和方式进行左右车道线的区域划分；最后通过Hough变换识别相应区域的直线段，将小于阈值的直线段用直线连接、大于阈值的利用贝塞尔曲线连接，以识别完整弯曲车道线。试验结果表明，该算法对大半径车道圆曲线的识别具有较好的稳健性。

Lane recognition of large radius circular curve based on vision

Lane line recognition is the key technology of lane keeping system in automatic driving. In order to improve the accuracy of lane line recognition of large radius circular curve, this algorithm divides the curved lanes on both sides into multiple regions, and uses straight line segment and Bezier curve to fit. Firstly, the image is preprocessed in the region of interest (ROI), and the turn is determined by the distribution of the feature pixels of the lane line according to the change law of the lane line. Then, the region of the left and right lane lines is divided according to different proportions and ways of the turn, and the line segments in the corresponding regions are identified by Hough transform, and the line segments less than the threshold are connected by lines. When the threshold value is higher than the set threshold value, the Bezier curve is used to connect and finally recognize the complete curved lane line. The experimental results show that the algorithm has good robustness to the recognition of large radius circular curve.