梯度点对约束的结构化车道检测

车道检测是无人驾驶车辆及车道偏离预警等系统的关键技术。针对复杂道路环境中,阴影、路面破损及车辆遮挡等常造成车道检测不准确的问题,提出利用结构化道路两侧车道线上常具有相反方向梯度的性质,将两侧车道线的检测转化为梯度点对约束下的车道中线及宽度检测。然后分别采用平行透视投影模型及线性双曲线模型,通过Hough变换获得了对车道消失点、宽度及车道中线等参数的估计,最终实现了对不同形状结构化车道的检测。比较了本文算法与其他两种车道检测算法在不同复杂道路环境下的车道线检测性能,结果表明了本文算法的有效性。

Gradient-pair constraint for structure lane detection

The lane detection is a key step in unmanned vehicles and lane departure warning system. For improving the reliability of lane detection under complicated situation, such as shadows, damaged pavement and vehicle occlusion, the characters of structure lane that the gradient of both side lane marker have opposite directions is utilized, and the lane detection problem is converted to detect the middle line and width of lane based on gradient-pair constraint. Then the parallel perspective model and linear-hyperbola model are used respectively to estimate the vanishing point coordinate, lane width and parameters of middle line by Hough transform. Compared with the other two algorithms under complicated lane situation in extensive experiments, the results demonstrate the effectiveness of the proposed method.