一种鲁棒性的多车道线检测算法

Robust Multi-lane Detection Algorithm

机器视觉被广泛应用于智能汽车领域,车道线检测成为人工智能领域内的研究热点。为了得到更具鲁棒性的车道线检测效果,采用一种基于多条件约束的车道线特征滤波器,并提出了一种新的对车道线特征进行聚类的算法。运用卡尔曼滤波对车道线位置进行实时跟踪和预测;利用基于透视投影线性关系的车道线"位置-宽度"函数设置自适应动态ROI;另外,利用车道线的特点形成约束条件以获得更加稳定的检测效果。在真实道路环境下的实时检测结果表明,该算法鲁棒性强、实时性好,且具有稳定的检测效果。

Machine vision is widely used in the field of intelligent vehicles,lane line detection has become a research hotspot in the field of artificial intelligence.In order to obtain a more robust lane-line detection effect,a lane-line feature filter based on multi-condition constraint was proposed,and a new clustering algorithm for lane-line feature was proposed.By using Kalman filter,the position and width of the lane line based on the perspective projection linear relationship are used to set the adaptive dynamic ROI.In addition,the constraint condition is obtained by using the characteristics of the lane line to obtain a more stable detection result.The real-time detection results in the real road environment show that the algorithm is robust and real-time,and has a very stable detection effect.