用于智能汽车的复杂光照环境车道线检测及跟踪方法

Detection and tracking method of lane line for intelligent vehicles under complex illumination condition

针对复杂光照环境的智能汽车行驶安全问题,提出一种车道线检测及跟踪的改进方法。在图像预处理阶段,应用灰度变换方法增大不同光照环境的车道线和道路对比度;用改进概率Hough变换方法提取二值化图像中的车道线;用最小二乘法进行车道线拟合;根据前一帧车道线检测结果建立Kalman滤波动态感兴趣区域,实现车道线准确跟踪。进行夜间光照、弱光照、强光照及正常光照等不同光照环境下的车道线检测和跟踪实验。结果表明:该方法的准确率约为97.53%,对于单帧车道图像的处理时间约为60 ms,具有较好的准确性和实时性,并具有抗路面阴影、交通标志、车辆遮挡、路灯等因素干扰的能力。

An improved lane line detection and tracking method was proposed for the driving safety of intelligent vehicles under complex illuminations.Gray-level transformation was used for image preprocessing to increase the contrast between lane lines and roads.Lane lines in binary images were extracted by using a progressive probabilistic Hough transform,and were fitted by using a least square method.Dynamic region of interest was established based on Kalman-filter according to the results of the previous image to track the lane lines.The lane line detection and tracking experiments were carried out under different illumination conditions,such as night light,weak light,normal light and strong light.The results show that this method has an accuracy of 97.53% and a real-time performance of 60 ms in the detection and tracking of lane lines,and has robustness against the interference of road shadow,traf fic signs,vehicles and other factors.