New lane detection method for autonomous driving

At present, traffic image detection technology represented by lane lines has emerged as a key area of research in the transportation. Extracting lane lines under complex backgrounds has become an urgent task for understanding road information features in technical fields such as unmanned driving. Previous studies have made great progress in feature extraction for clear target detection. However, these studies cannot be well applied to the task of accurate lane edge detection, and the accuracy of individual feature detection will be lost during the fusion training process. In view of the diverse and complex characteristics of road information within the driving field of vision, this paper designs a lane detection method that combines target classification and K-means clustering methods. To improve the lane line's longdistance detection ability, the YOLOv8, which relies on the attention mechanism, is first built to identify the lane line. Secondly, through feature extraction, the lane line accurate segmentation based on the improved K-means is constructed, with the aim of the semantic information of the lane line is fused with the local features, which further improves the reliability and swiftness of lane line recognition. According to experimental findings, the suggested approach shows certain improvements in accuracy, recall rate and F1 measure compared with the popular lane line extraction method.