Real-time object classification for autonomous vehicle using LIDAR

Object classification is an important issue in order to bring autonomous vehicle into reality. In this paper, real-time and robust classification based on Real AdaBoost algorithm is researched and improved. Various effective features of road objects are computed using LIDAR 3D point clouds. The improved classifier provides an accuracy of over 90 (%) in a range of 50 (m) and classifies objects into car, pedestrian, bicyclist and background. Moreover, processing time of classifying an object consumes only 0.07×10
^-3
(sec) that enables this method to be used for autonomous driving on urban roads.