Robust Lane Detection under Varying Lighting Conditions Using Adaptive Vision-Based Techniques

Reliable lane detection is crucial to autonomous driving but continues to be challenging with varying lighting conditions. Fluctuations in illuminations due to bright sunlight, shadows or low lighting at night can degrade the visual quality and adversely affect the accuracy of the lane detection results. This research proposes an adaptive approach for lane detection under different lighting scenarios. For daytime, a Region of Interest (ROI) masking and line averaging technique help in the stability and visibility of the lane markings. For nighttime conditions, a Probabilistic Hough Transform-based method improves lane detection in low-light environments. An evaluation tool has been developed to check if certain parameters correlate with day or night to enable dynamic selection of the most suitable detection technique. The proposed new method improves image preprocessing and combines several computer vision algorithms for accurate lane tracing. This new solution aids in shadow regions and faded marking areas, as well as improves precision for multi-lane roadways with varying lane widths. The approach adds accuracy for real-time lane recognition of autonomous vehicles on multi-lane highways with different degrees of illumination. This research also contributes toward the goal of improving safety and efficiency in autonomous driving by providing more effective methods of ensuring safe driving.