Real‐time vehicle detection and tracking using 3D LiDAR

Abstract

AbstractThis paper studies the problem of vehicle detection and tracking for an autonomous vehicle using 3D Light Detection and Ranging (LiDAR). In order to increase the accuracy of vehicle detection and tracking, a new clustering algorithm is proposed to obtain vehicle candidates from preprocessed point cloud data collected by the LiDAR. A classifier trained by the support vector machine (SVM) algorithm is used to detect vehicles from vehicle candidates. Kalman filter and global nearest neighbor (GNN) algorithm are used to track vehicles, and the accuracy of vehicle detection result is further improved by the aid of tracking results. The proposed method has been verified on a testing platform.