An effective and fast lane detection algorithm

Chung Yen Su, Gen Hau Fan

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

10 Citations (Scopus)

Abstract

Lane detection is crucial for autonomous driving. In this paper, we present an effective and fast lane detection algorithm. The proposed algorithm includes three novelties. First, we set a region of interest (ROI) appropriate to reduce nonessential cost of computation. Second, we determine a real midpoint between two road lines for each frame. The midpoint can be used to classify the candidates of lane marking points to right and left effectively. Finally, we use a temporal trajectory strategy to avoid the failure of lane detection, which is generally caused by shadows of bridges or neighboring vehicles. Experimental results show that the proposed algorithm can label the location of lane marking accurately and fast. It processes a frame only 16 ms and can solve the problems caused by lighting change, shadows, and vehicle occlusions.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings
Pages942-948
Number of pages7
EditionPART 2
DOIs
Publication statusPublished - 2008 Dec 1
Event4th International Symposium on Visual Computing, ISVC 2008 - Las Vegas, NV, United States
Duration: 2008 Dec 12008 Dec 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5359 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Symposium on Visual Computing, ISVC 2008
CountryUnited States
CityLas Vegas, NV
Period08/12/108/12/3

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Su, C. Y., & Fan, G. H. (2008). An effective and fast lane detection algorithm. In Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings (PART 2 ed., pp. 942-948). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5359 LNCS, No. PART 2). https://doi.org/10.1007/978-3-540-89646-3_94