Vision-Based Lane Detection and Tracking under Different Challenging Environmental Conditions

Lane marking detection is fundamental for both advanced driving assistance systems and traffic surveillance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low, obscured or often invisible due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e. shadow, glare of light, reflection etc. created by different light sources like streetlamp, tunnel-light, sun, wet road etc.; (ii) Obscured visibility of eroded, blurred, dashed, colored and cracked lane caused by natural disasters and adverse weather; (iii) lane marking occlusion by different objects from surroundings; and (iv) presence of confusing lines e.g., guardrails, pavement marking, road divider etc. In this paper, we proposed a simple, real-time, and robust lane detection and tracking method to detect and track lane marking. Here, we introduced three key technologies. First, we introduce a comprehensive intensity threshold range (CITR) to improve the performance of the canny operator in detecting lane edges of different intensity. Second, we propose a robust lane verification technique, the angle and length-based geometric constraint (ALGC) followed by Hough Transform, to verify the characteristics of lane marking and to prevent incorrect lane detection. Finally, we propose a novel lane tracking technique, to predict the lane position of next frame by defining a range of horizontal lane position which will be updating with respect to the lane position of previous frame. To evaluate the performance of the proposed method we used the DSDLDE [1] dataset with 1080x1920 resolutions at 24 frames/sec. Experimental results show that the average detection rate is 97.36%, and the average detection time is 29.06msec per frame, which outperformed the state-of-the-art method.