AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads

Hyunsung Lee; Seonwook Kim; Sungyoul Park; Yonghwan Jeong; Hojoon Lee; Kyongsu Yi

doi:10.1109/IVS.2017.7995911

AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads

Hyunsung Lee, Seonwook Kim, Sungyoul Park, Yonghwan Jeong, Hojoon Lee, Kyongsu Yi

Dept. of Mechanical & Automotive Engineering

Seoul National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

Abstract

This paper proposes a lane marking detection method for automated driving in complex urban roads to be used with map-matching localization algorithms. First, a LiDAR based lane detection and map matching algorithm is explained and a lane marking detection algorithm using AVM (Around View Monitor) cameras is presented. The AVM camera based lane marking detection algorithm includes camera calibration, bird-eye view conversion, and lane marking detection. The two lane detection methods are combined and implemented as a part of a map-matching localization algorithm on an autonomous test vehicle. Multiple experiments on various test routes, including complex situations like driver's license exam stations, were performed to verify the performance of the lane marking detection in the map-matching algorithm.

Original language	English
Title of host publication	IV 2017 - 28th IEEE Intelligent Vehicles Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1434-1439
Number of pages	6
ISBN (Electronic)	9781509048045
DOIs	https://doi.org/10.1109/IVS.2017.7995911
State	Published - 28 Jul 2017
Event	28th IEEE Intelligent Vehicles Symposium, IV 2017 - Redondo Beach, United States Duration: 11 Jun 2017 → 14 Jun 2017

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference	28th IEEE Intelligent Vehicles Symposium, IV 2017
Country/Territory	United States
City	Redondo Beach
Period	11/06/17 → 14/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IVS.2017.7995911

Cite this

Lee, H., Kim, S., Park, S., Jeong, Y., Lee, H., & Yi, K. (2017). AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads. In IV 2017 - 28th IEEE Intelligent Vehicles Symposium (pp. 1434-1439). Article 7995911 (IEEE Intelligent Vehicles Symposium, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2017.7995911

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title = "AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads",

abstract = "This paper proposes a lane marking detection method for automated driving in complex urban roads to be used with map-matching localization algorithms. First, a LiDAR based lane detection and map matching algorithm is explained and a lane marking detection algorithm using AVM (Around View Monitor) cameras is presented. The AVM camera based lane marking detection algorithm includes camera calibration, bird-eye view conversion, and lane marking detection. The two lane detection methods are combined and implemented as a part of a map-matching localization algorithm on an autonomous test vehicle. Multiple experiments on various test routes, including complex situations like driver's license exam stations, were performed to verify the performance of the lane marking detection in the map-matching algorithm.",

author = "Hyunsung Lee and Seonwook Kim and Sungyoul Park and Yonghwan Jeong and Hojoon Lee and Kyongsu Yi",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 28th IEEE Intelligent Vehicles Symposium, IV 2017 ; Conference date: 11-06-2017 Through 14-06-2017",

year = "2017",

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day = "28",

doi = "10.1109/IVS.2017.7995911",

language = "English",

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Lee, H, Kim, S, Park, S, Jeong, Y, Lee, H & Yi, K 2017, AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads. in IV 2017 - 28th IEEE Intelligent Vehicles Symposium., 7995911, IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1434-1439, 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11/06/17. https://doi.org/10.1109/IVS.2017.7995911

AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads. / Lee, Hyunsung; Kim, Seonwook; Park, Sungyoul et al.
IV 2017 - 28th IEEE Intelligent Vehicles Symposium. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1434-1439 7995911 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lee, Hyunsung

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N2 - This paper proposes a lane marking detection method for automated driving in complex urban roads to be used with map-matching localization algorithms. First, a LiDAR based lane detection and map matching algorithm is explained and a lane marking detection algorithm using AVM (Around View Monitor) cameras is presented. The AVM camera based lane marking detection algorithm includes camera calibration, bird-eye view conversion, and lane marking detection. The two lane detection methods are combined and implemented as a part of a map-matching localization algorithm on an autonomous test vehicle. Multiple experiments on various test routes, including complex situations like driver's license exam stations, were performed to verify the performance of the lane marking detection in the map-matching algorithm.

AB - This paper proposes a lane marking detection method for automated driving in complex urban roads to be used with map-matching localization algorithms. First, a LiDAR based lane detection and map matching algorithm is explained and a lane marking detection algorithm using AVM (Around View Monitor) cameras is presented. The AVM camera based lane marking detection algorithm includes camera calibration, bird-eye view conversion, and lane marking detection. The two lane detection methods are combined and implemented as a part of a map-matching localization algorithm on an autonomous test vehicle. Multiple experiments on various test routes, including complex situations like driver's license exam stations, were performed to verify the performance of the lane marking detection in the map-matching algorithm.

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M3 - Conference contribution

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T3 - IEEE Intelligent Vehicles Symposium, Proceedings

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AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads

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