针对在复杂的工况下车道线检测的鲁棒性和实时性较差等问题，本文通过融合边缘检测与多颜色空间阈值分割结果，进行车道线特征点的提取. 结合车道线在鸟瞰图中的位置特点，提出了基于DBSCAN二次聚类（Reclustering based on Density-Based Spatial Clustering of Application with Noise，RC-DBSCAN）的特征点聚类算法. 并以簇点是否进行二次聚类和Lab空间采样簇点的平均灰度值为依据，进行车道线线型和颜色的识别. 使用最小二乘法对车道线进行拟合，通过基于可信区域的卡尔曼滤波算法对拟合后的车道线进行跟踪. 最后在实际道路采集的视频与公开的数据集中进行了实验. 实验表明，本文算法在复杂路况下对车道线检测的鲁棒性优于传统聚类算法，实时性能够满足实际需求；在结构化道路上，对车道线类型的识别也具有很高的准确率.
Abstract:
In view of the poor robustness and real-time performance of lane detection under complex working conditions，this paper extracts the feature points of lane line by fusing the results of edge detection and multi-color space threshold segmentation. Combined with the location characteristics of lane line in aerial view，a feature point reclustering algorithm based on RC-DBSCAN （Reclustering based on Density-Based Spatial Clustering of Application with Noise） is proposed. Based on whether the cluster points are clustered twice or not and the average gray value of the cluster points sampled in Lab space，the lane line shape and color are identified. The lane line is fitted by the least square method，and the fitted lane line is tracked by the Kalman filter algorithm based on the trusted region. Finally，the experiment is carried out in the real road video and public data set. Experimental results show that the robustness of the proposed algorithm is better than the traditional clustering algorithm in complex road conditions，and the real-time performance can meet the actual needs；on the structured road，the recognition of lane type also has high accuracy.
