Illumination-Resilient Lane Detection by Threshold Self-Adjustment Using Newton-Based Extremum Seeking

The ability to detect lane markings under varying lighting conditions is essential for autonomous mobile robots and automatic vehicle driving assistance systems. Because the object color information is subject to illumination variation, this article presents a novel and computationally efficient algorithm based on the extremum-seeking method to achieve illumination-resilient lane detection and path-following tasks for autonomous driving. Lane detection is performed in the hue-saturation-value color space by distinguishing the colored lane marks from the background. The system’s inputs are the upper and lower thresholds in each of the hue, saturation, and value channels. We define a cost function as the combination of detection accuracy and lane coverage to evaluate the algorithm performance. Two extremum-seeking schemes, one with fixed dither amplitudes and another with adaptive dither amplitudes, are designed to adjust the system inputs to minimize the cost function. The two proposed methods are validated and compared through video-based simulation studies and scaled-car field experimental tests.