基于分段切换模型的点集优化车道线识别方法

针对车辆的横向运动偏差及相关安全监控技术存在缺陷的问题,提出一种基于分段切换模型的点集优化车道线识别方法。首先建立稳定可靠的分段切换车道线模型,然后利用单目视觉和毫米波雷达的传感器组合对车辆进行识别,筛选掉干扰车道线识别的无效候选点,如果缺少有效的识别点,则利用卡尔曼滤波和车道线等宽条件进行有效点的补充。实验证明,该方法能够准确有效的识别车道线,对于减少车辆行驶过程中安全事故的发生具有重要意义。

Point set optimization lane line identification method based on the subsection switch model

In view of the vehicle lateral movement deviation and the defects of relevant safety monitoring technology, Put forward a point set optimization lane line identification method based on the subsection switch model. First established subsection switch lanes line model of stable and reliable. Then using monocular vision and millimeter wave radar sensor combination to SidentifySvehicle. Eliminated the interference information of the lane line candidate point. If effective lane line pointsSinsufficient. using kalman filter and lane line width conditionSto supplement the efficient point. Experiments show that point set optimization identification method based on the subsection switch model can identify the lane line effectively and hasSgreat significant toSreduce vehicle traffic safety accidentsSin the process.