摘要
提出了一种光照不变的车道线检测算法,充分利用时空上下文信息实现了鲁棒的车道线检测.通过霍夫变换自适应检测出消失点和感兴趣区域,减少计算量;利用图像序列的时空上下文信息,以及一种时间切片图像的产生方法来获得车道线关键点,此外,利用顶帽变换增强图像对比度以获得噪声和低光照度情况下更好的分割效果;给出了车道线预测的方法.通过若干试验验证,结果表明,该算法可以有效地检测出车道线,并且对于低光照、变光照、破损和模糊车道线具有较好的鲁棒性.
Lane detection is a crucial component of many advanced driver-assistance systems.Lane position can help both develop many driving assistance systems such as lane keeping system(LKS),lane departure warning system(LDWS)and achieve vehicle local location and behavior prediction.In this paper,an illumination invariant lane detection method was proposed,making full use of time-space context for robust lane detection.First,the vanishing point was detected adaptively in the strip image by using standard Hough transform.Thus,the region of interest(ROI)can be obtained adaptively for computation reduction. Then,considering the time-context information of the image sequences,aprocedure for generating the time slice image used to acquire the lane marks key points was designed.In addition,tophat processing was utilized to enhance contrast degree of the time slice image for better segmentation between the lane marks and road under the noise and low illumination.Finally,the method for predicting the position of current lane marks was given.Several driving scenarios were tested.The experiment results show that the proposed lane detection method works well with acceptable performance and has enough tolerance to low illumination,variant illumination,and broken and blurred lanes.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第S1期31-36,共6页
Journal of Tongji University:Natural Science
基金
国家重点研发计划(2016YFB0101101)
