新能源汽车无人驾驶障碍检测过程中，受道路上的多种障碍物以及复杂的天气如雨雪、雾霾等影响，数据采集难度较大，导致驾驶障碍检测精度偏低。为此，提出车载激光雷达下新能源汽车无人驾驶障碍检测方法。采取水平安装的方式，将两个32线激光雷达以对称方式布置在无人驾驶汽车上，采集路面结构三维点云数据并用坐标转换同步数据;基于统计特征的离散点滤波算法，计算点云数据集的标准差和全局距离平均值，以此去除离散噪声点;采用基于密度的带有噪声的空间聚类(Density Based Spatial Clustering of Applications with Noise，DBSCAN)聚类算法实现新能源汽车无人驾驶的障碍检测。实验结果表明，所提方法抗噪能力较高，平均识别距离为82 m，障碍检测准确率均在92％以上，误检率最高为1。8％，且检测耗时仅为1。09 ms。
Obstacle detection for unmanned driving of new energy vehicles using onboard LiDAR
In the process of driverless obstacle detection for new energy vehicles,it is difficult to collect data due to various obstacles on the road and complex weather such as rain,snow and haze,resulting in low accuracy of driving obstacle detection.To this end,a method for detecting obstacles in unmanned driving of new energy vehicles using on-board LiDAR is proposed.Adopting a horizontal installation method,two 32 line LiDARs are symmetrically arranged on unmanned vehicles to collect 3D point cloud data of road structure and synchronize the data with coordinate conver-sion.A discrete point filtering algorithm based on statistical features calculates the standard deviation and global dis-tance average of point cloud datasets to remove discrete noise points.Using Density Based Spatial Clustering of Appli-cations with Noise(DBSCAN)clustering algorithm to achieve obstacle detection for autonomous driving of new energy vehicles.The experimental results show that the proposed method has high noise resistance,an average recognition dis-tance of 82 m,obstacle detection accuracy of over 92％,and the highest false detection rate of 1.8％.The detection time is only 1.09 ms.
vehicle mounted LiDARunmanned drivingpoint cloud data collectiondiscrete noiseDBSCAN clustering
NETL
NSTL
万方数据
