LAPSE:2023.25675
Published Article
LAPSE:2023.25675
A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision
Darko Babić, Dario Babić, Mario Fiolić, Arno Eichberger, Zoltan Ferenc Magosi
March 29, 2023
Lateral support systems in vehicles have a high potential for reduction of lane departure crashes. To profit from their full potential, such systems should function properly in adverse conditions. Literature indicates that their accuracy varies between day and night-time. However, detailed quantifications of the systems’ performance in these conditions are rare. The aim of this study is to investigate the differences in detection quality and view range of Mobileye 630 in dry daytime and night-time conditions. On-road tests on four rural road sections in Croatia were conducted. Wilcoxon signed-rank test was used to test the difference between the number of quality rankings while absolute average, average difference and standard deviation were used to analyse the view range. Also, a paired samples t-test was used to test the difference between conditions for each line on each road. The overall results confirm that a significant difference in lane detection quality view range exists between tested conditions. “Medium” and “high” detection confidence (quality level 3 and 2), increased by 5% and 8% during night-time compared to daytime while level 0 (“nothing detected”) decreased by 12%. The view range increased (almost 16% for middle line) during daytime compared to night-time. The findings of this study expand the existing knowledge and are valuable for research and development of machine-vision systems but also for road authorities to optimize the markings’ quality performance.
Keywords
ADAS, automated driving, lane detection, lane keeping systems, lateral support systems, visibility
Subject
Suggested Citation
Babić D, Babić D, Fiolić M, Eichberger A, Magosi ZF. A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision. (2023). LAPSE:2023.25675
Author Affiliations
Babić D: Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
Babić D: Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
Fiolić M: Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
Eichberger A: Institute of Automotive Engineering, Graz University of Technology, Inffeldgasse 11/II, A-8010 Graz, Austria [ORCID]
Magosi ZF: Institute of Automotive Engineering, Graz University of Technology, Inffeldgasse 11/II, A-8010 Graz, Austria
Journal Name
Energies
Volume
14
Issue
15
First Page
4666
Year
2021
Publication Date
2021-08-01
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14154666, Publication Type: Journal Article
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LAPSE:2023.25675
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doi:10.3390/en14154666
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Mar 29, 2023
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CC BY 4.0
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Mar 29, 2023
 
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