Asian Journal of Control · 2021 · 41 citations · 25 references
ComputingAutonomous SystemsComputer ScienceAutonomous VehiclesComputer VisionMachine LearningLidar PerceptionObject DetectionPattern RecognitionPoint Cloud ProcessingSupport Vector MachinesVisual TrackingClusteringGlobal Nearest NeighborLidar Data ProcessingLidar Point Cloud ProcessingLidar SensingSupport Vector ClassificationSupport Vector Machine ClassificationVehicle Detection
Abstract This 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.
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