高速路的车道检测与车辆跟踪

基于智能交通的快速发展,研究了基于高速路的车道检测和车辆跟踪技术.对于多车道检测,根据路面与分道线灰度级相差较大的特点来实现车道路面的分割,接着结合直线方程和Catmull-Rom Spline插值算法来拟合分道线.对于单车道检测,首先基于HSV颜色空间和Sobel边缘提取方法对其进行有效分割,接着在透视变换空间中提取分道线坐标点并用二次多项式拟合分道线.针对车辆检测,使用Hog+Gentle-Adaboost分类算法实现无人车前方路面车辆的检测,接着基于车底阴影的特征对车底阴影进行检测以验证学习算法检测到的车辆区域的真伪性.针对车辆跟踪,采用动态二阶自回归模型的方法预测车辆的状态.其中,对于粒子滤波固有的粒子退化问题,引入Thompson_Taylor算法改善了粒子退化和低多样性的缺陷.本文的车道检测和车辆跟踪算法能较容易地移植在嵌入式平台,可靠性和准确性较高,且有助于进一步实现车道偏离报警和前向防撞系统.

Lane Detection and Vehicle Tracking on Highway

Based on the rapid development of intelligent transportation, this work studies the lane detection and vehicle tracking technology of high-speed sections. For multi-lane detection, the road surface is segmented by using the feature that the gray level difference between the road surface and the dividing line is rather large. Then, the line equation and the Catmull-Rom Spline interpolation algorithm are used to fit the lane dividing line. For single-lane detection, the single lane is first effectively segmented based on the HSV color space and Sobel edge extraction method, and then the lane separation coordinate points are extracted in the perspective transformation space and the segmentation line is fitted with a quadratic polynomial. Aiming at the vehicle detection, the HOG+Gentle-Adaboost classification algorithm is firstly used to detect the vehicle in front of the unmanned vehicle, and then the shadow of the vehicle is detected based on the characteristics of the shadow at the bottom to verify the authenticity of the vehicle area detected by the learning algorithm. For vehicle tracking, the dynamic second-order autoregressive model method is used to predict the state of the vehicle. For the inherent particle degradation problem of particle filtering, this study innovatively introduces the Thompson-Taylor algorithm to improve the defects of particle degradation and low diversity. The lane detection and vehicle tracking algorithms in this study can be easily transplanted on the embedded platform with high reliability and accuracy, and further to realize the lane departure warning and forward collision avoidance system.