Road users classification technology based on roadside light detection and ranging

Abstract

Road users classification plays an important role in the transportation management. With lower price of LiDAR, road user classification based on roadside Light Detection and Ranging (LiDAR) data has been a new approach in the transportation field. It is also essential for other intelligent transportation technology. In this paper, a method developed for road user classifications was proposed with roadside LiDAR data. The proposed method can be divided into four parts: background filtering, point clustering, feature selection, and road user classification. Five features of road users were selected based on the characteristic of road users’ point clouds. A comprehensive database was established and the classification performance of five machine learning methods including random forest, support vector machine, Probabilistic neural network, back propagation neural network, and AdaBoost were evaluated by F1score and F1macro. The results presented that AdaBoost had the best classification performance than others (The value of <i>F₁macro</i> was 0.642).