論文

査読有り
2018年2月2日

Real-time object classification for autonomous vehicle using LIDAR

ICIIBMS 2017 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences
  • Masaru Yoshioka
  • ,
  • Naoki Suganuma
  • ,
  • Keisuke Yoneda
  • ,
  • Mohammad Aldibaja

2018-
開始ページ
210
終了ページ
211
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ICIIBMS.2017.8279696
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

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 0.0710-3 (sec) that enables this method to be used for autonomous driving on urban roads.

リンク情報
DOI
https://doi.org/10.1109/ICIIBMS.2017.8279696
URL
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047391785&origin=inward
ID情報
  • DOI : 10.1109/ICIIBMS.2017.8279696
  • SCOPUS ID : 85047391785

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