Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?

Author:

Storsæter Ane DalsnesORCID,Pitera KellyORCID,McCormack Edward

Abstract

Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving.

Funder

Statens vegvesen

Publisher

MDPI AG

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. METRO: Magnetic Road Markings for All-weather, Smart Roads;Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems;2023-11-12

2. Enhancing Driver Safety through Real-Time Feedback on Driving Behavior: A Deep Learning Approach;2023 International Conference on Advanced Computing Technologies and Applications (ICACTA);2023-10-06

3. Machine Learning Method for Road Vehicle Collected Data Analysis;Journal of Applied Meteorology and Climatology;2023-06

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