基于激光雷达的自动驾驶车辆实时目标分类

2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2017-11-01 DOI:10.1109/ICIIBMS.2017.8279696

Masaru Yoshioka, N. Suganuma, Keisuke Yoneda, Mohammad Aldibaja

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引用次数: 27

摘要

目标分类是实现自动驾驶汽车的一个重要问题。本文研究并改进了基于Real AdaBoost算法的实时鲁棒分类。利用激光雷达三维点云计算道路物体的各种有效特征。改进后的分类器在50米范围内提供了超过90%的准确率，并将物体分为汽车、行人、自行车和背景。此外，分类对象的处理时间仅为0.07∗10−3 (sec)，使该方法能够用于城市道路上的自动驾驶。

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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.

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2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

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