Long-Range Vehicle Detection of LiDAR using Adaboost Classification

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering- based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and fore- ground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generat- ing 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments.