Lane detection techniques for self-driving vehicle: comprehensive review

According to WHO, 1.35 million people, every year are cut short in road accidents, most of them caused due to human misconduct and ignorance. To improve safety over the roads, road perception and lane detection play a crucial part in avoiding accidents. Lane Detection is a constitution for various Advanced Driver Assisting System (ADAS) like Lane Keeping Assisting System (LKAS) and Lane Departure Warning System (LDWS). It also enables fully assistive and autonomous navigation in self-driving vehicles. Therefore, it has been an effective field of research for the past few decades, but various milestones are yet to be achieved. The problem has encountered various challenging scenarios due to the past limitations of resources and technologies. In this paper, we reviewed the different approaches based on image processing and computer vision that have revolutionized the lane detection problem. This paper also summarizes the different benchmark data sets for lane detection, evaluation criteria. We implemented Lane detection system using Unet and Segnet model and applied it on Tusimple dataset. The Unet performance is better as compared to Segnet model. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm. Finally, we compare various researcher's approaches with their performances. This paper concluded with the challenges to predict accurate lanes under different scenarios.