An effective and fast lane detection algorithm

Chung-Yen Su, Gen Hau Fan

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

8 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

Fingerprint

Lane Detection
Midpoint
Region of Interest
Occlusion
Labels
Lighting
Classify
Trajectories
Trajectory
Line
Costs
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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

An effective and fast lane detection algorithm. / Su, Chung-Yen; Fan, Gen Hau.

Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings. PART 2. ed. 2008. p. 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).

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

Su, C-Y & Fan, GH 2008, An effective and fast lane detection algorithm. in Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5359 LNCS, pp. 942-948, 4th International Symposium on Visual Computing, ISVC 2008, Las Vegas, NV, United States, 08/12/1. https://doi.org/10.1007/978-3-540-89646-3_94
Su C-Y, Fan GH. An effective and fast lane detection algorithm. In Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings. PART 2 ed. 2008. p. 942-948. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-540-89646-3_94
Su, Chung-Yen ; Fan, Gen Hau. / An effective and fast lane detection algorithm. Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings. PART 2. ed. 2008. pp. 942-948 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{ea564fa9e1f5402c8b73d19fe377b382,
title = "An effective and fast lane detection algorithm",
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.",
author = "Chung-Yen Su and Fan, {Gen Hau}",
year = "2008",
month = "12",
day = "1",
doi = "10.1007/978-3-540-89646-3_94",
language = "English",
isbn = "3540896457",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "942--948",
booktitle = "Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings",
edition = "PART 2",

}

TY - GEN

T1 - An effective and fast lane detection algorithm

AU - Su, Chung-Yen

AU - Fan, Gen Hau

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70149122751&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70149122751&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-89646-3_94

DO - 10.1007/978-3-540-89646-3_94

M3 - Conference contribution

AN - SCOPUS:70149122751

SN - 3540896457

SN - 9783540896456

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 942

EP - 948

BT - Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings

ER -