Lane Marking Detection for Highway Scenes based on Solid-state LiDARs

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information. According to the priori geometric features of lane markings, a series of post-processing algorithms are designed to further improve the performance and finally output accurate lane marking curve information, with detection range up to more than 100 meters. Our method is evaluated with the highway testing data collected by a self-driving heavy truck from Hong Jing Drive Company.