New lane detection method for autonomous driving

Engineering notes

Key topics: autonomous driving, lane detection. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page

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