Due to uneven lighting in complex lighting environments, the intensity of light on the road surface varies, resulting in changes in the brightness and color of lane lines, making lane line detection difficult. In order to improve the accuracy of lane detection, a lane detection algorithm for complex lighting environments is proposed. The algorithm is mainly based on the texture features of lane lines, and uses voting algorithms to extract feature points from the vanishing points of images on traffic roads, constructing key detection areas for lane lines; Design an ant colony algorithm that combines gradient and statistical average relative difference for lane edge feature extraction, and finally fit and label the lane line to be tested. The experimental results show that the proposed method has high detection accuracy and strong robustness in complex lighting environments, significantly improving the success rate of lane detection under complex lighting conditions.