**5. Conclusions**

The results of this study indicate that lane marking detection quality and view range by machine-vision system differs to some extent between dry daytime and night-time conditions. The field test results show that detection quality of lane markings is "better" during night-time compared to daytime. However, it has to be noted that the aforementioned differences are relatively small and may not critically influence the functioning of the lane detection system. Nevertheless, the results presented here support previous findings and provide further proof that visibility conditions play an important role in lane detection and that, during daytime, the complexity of visual clutter decreases the contrast ratio between the marking and the road surface and thus affects the detection quality and view range of machine-vision. In addition, the results suggest that other factors such as road geometry, markings' age and quality are also influential and should be further evaluated.

Overall, the findings of this study provide a quantization of the effect of surrounding visibility on lane detection and thus contribute to expanding the existing knowledge regarding lane detection by machine-vision. Here presented methodology and results may be useful for researchers in designing and evaluating similar studies. Furthermore, the results may be useful to road authorities. Although, the study did not evaluate how different visibilities of road markings affect their detection by machine-vision, the results support previous findings which indicate that the detection of lane markings is much more problematic for machine-vision during daytime compared to night-time. This finding is useful for road authorities and may help them in prioritizing and optimizing road marking maintenance activities. Depending on the weather and traffic conditions and general road characteristics (type of the road, width, general road geometry, surface condition etc.), road authorities should adopt different maintenance policies to ensure proper and timely maintenance and thus adequate quality of road markings needed for both for human drivers as well as machine-vision systems. Finally, the presented findings may help the developers of machine-vision systems in detecting critical situations and conditions which negatively affect lane marking detection.

**Author Contributions:** Conceptualization, D.B. (Darko Babi´c), D.B. (Dario Babi´c) and M.F.; methodology, D.B. (Darko Babi´c), D.B. (Dario Babi´c) and M.F.; formal analysis, D.B. (Dario Babi´c) and M.F.; investigation, D.B. (Darko Babi´c), D.B. (Dario Babi´c), M.F., A.E. and Z.F.M.; resources, D.B. (Darko Babi´c) and A.E.; data curation, D.B. (Dario Babi´c) and Z.F.M.; writing—original draft preparation, D.B. (Dario Babi´c), M.F., A.E.; visualization, D.B. (Dario Babi´c); supervision, D.B. (Darko Babi´c); project administration, D.B. (Darko Babi´c); funding acquisition, D.B. (Darko Babi´c). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was a part of the project entitled "Establishing a Methodology for Testing and Evaluating the ADAS Systems" funded by University of Zagreb (Potpore za temeljno financiranje znanstvene i umjetniˇcke djelatnosti Sveuˇcilišta u Zagrebu u ak. god. 2019/2020).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

**Conflicts of Interest:** The authors declare no conflict of interest.
