Robust Lane Detection and Tracking for Autonomous Driving of Rubber-Tired Gantry Cranes in a Container Yard

Autonomous driving, remote operation, and driver assistance of rubber-tired gantry cranes (RTG) are vital to the automation of container terminals. Robust and reliable lane detection and tracking are crucial links in them. This paper proposes a novel lane detection and tracking method based on traditional image process techniques instead of deep-learning -based approaches that rely heavily on large-scale datasets. It utilizes the structural characteristics of a container yard. First, we propose an adaptive sliding window method to resolve complex issues of lane detection suffering from smudges, occlusions, and breakages. Furthermore, the continuity and structural constraints of inter-frames of lanes are explored and used to improve the stability of lane tracking. Then, extensive experiments are conducted to detect contaminated, occluded, and separate lane lines under different lighting conditions and to test the robustness of lane tracking with severe disturbances. The results show that the proposed method can attain robust lane detection and track for RTG autonomous driving in a container yard.