Performance evaluation of a lane departure warning system

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

Abstract

This paper focuses on a night time driving performance evaluation of an image-based lane departure warning (LDW) system which was previously developed by the author [25, 26]. In the previous study, road testing was only conducted under the day time driving condition. So, the main focus here is to conduct night time driving evaluations. This system uses the Lucas-Kanada (L-K) optical flow and the Hough transform methods approach. Based on the status of the vehicle deviating from its heading lane, the method integrates both techniques to establish an operation algorithm to determine whether a warning signal should be issued. The L-K optical flow tracking is used when the lane boundaries cannot be detected, while the lane detection technique is used when they become available. Even though both techniques are used in the system, only one method is activated at any given time because each technique has its own advantages and also disadvantages. The image-based LDW system was road tested on rural highways and this paper briefly presents our night time driving test results.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1768-1773
Number of pages6
ISBN (Electronic)9781538621035
DOIs
StatePublished - Feb 5 2018
Externally publishedYes
Event12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017 - Siem Reap, Cambodia
Duration: Jun 18 2017Jun 20 2017

Publication series

NameProceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
Volume2018-February

Other

Other12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
CountryCambodia
CitySiem Reap
Period6/18/176/20/17

Bibliographical note

Funding Information:
* Resrach supported by ITS Institute, University of Minnesota’s Center for Transportation Studies (CTS). Financial support was provided by the US Department of Transportation (USDOT)’s Research and Innovative Technologies Administration (RITA).

Funding Information:
ACKNOWLEDGMENT The research was funded by the Intelligent Transportation Systems (ITS) Institute, a program of the University of Minnesota’s Center for Transportation Studies (CTS). Financial support was provided by the United States Department of Transportation (USDOT)’s Research and Innovative Technologies Administration (RITA). The project was also supported by the Northland Advanced Transportation Systems Research Laboratories (NATSRL), a cooperative research program of the Minnesota Department of Transportation (MnDOT) and the ITS Institute. The author would also like to thank Gregory Taubel and Rohit

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