Vision-based driving environment identification for autonomous highway vehicles

Yao Jan Wu, Chun Po Huang, Feng Li Lian, Tang Hsien Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose an approach to identify the driving environment for autonomous highway vehicles by means of image processing and computer vision techniques. The proposed approach is mainly composed of two consecutive computational steps. The first step is the lane markings detection, used to identify the location of host vehicle and road geometry. The second one is the vehicle detection that can provide relative position and speed between host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenes. Herein, the experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

Original languageEnglish (US)
Title of host publicationConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
Pages1323-1328
Number of pages6
StatePublished - Jun 28 2004
Externally publishedYes
EventConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control - Taipei, Taiwan, Province of China
Duration: Mar 21 2004Mar 23 2004

Publication series

NameConference Proceeding - IEEE International Conference on Networking, Sensing and Control
Volume2

Other

OtherConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
CountryTaiwan, Province of China
CityTaipei
Period3/21/043/23/04

Keywords

  • Computer vision
  • Driving environment identification
  • Image processing
  • Lane markings detection
  • Vehicle detection

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Wu, Y. J., Huang, C. P., Lian, F. L., & Chang, T. H. (2004). Vision-based driving environment identification for autonomous highway vehicles. In Conference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control (pp. 1323-1328). (Conference Proceeding - IEEE International Conference on Networking, Sensing and Control; Vol. 2).