## 5 Conclusions  
This paper aimed to identify daytime and nighttime periods in video footage and set optimal parameters for lane detection under challenging conditions. The proposed approach involved utilizing the restriction system to determine the most probable lane lines from the detected candidates in the edge image. The position information was then updated using the Kalman filter to predict the lane position in the next frame.  
In the experiments, it was observed that the proposed approach exhibited slightly more stability compared to conventional methods, U-Net ConvLSTM, and DSUNet. This confirms the effectiveness of distinguishing between daytime and nighttime periods, setting appropriate parameters, maximizing contrast using MSR, and selecting lanes with the proposed restriction system, achieving successful and stable lane detection in challenging conditions.  
However, the proposed approach in this paper has limitations in detecting curved lanes, as the Hough transform used can only identify straight lines in the edge image. To address this limitation, this paper suggests exploring pixel-level segmentation techniques for accurate detection of curved lanes. This can involve subdividing the lane-detection area into smaller segments and representing curves using multiple lines or adopting alternative approaches.  
As demonstrated in this paper, deep learning is not always superior in all scenarios. The experiments revealed that while deep learning requires powerful GPUs, extensive datasets (400,000 images), and long training times, the proposed approach achieved comparable or even better results without the need for dedicated GPU devices, large datasets, or extensive training time. This highlights the practicality and effectiveness of the proposed approach in challenging lane-detection scenarios.  
Figure 21: Examples of evaluation criteria for lane detection (Yellow: conventional, Purple: DSUNet, Red: U-Net ConvLSTM, Green: proposed method). (**a**) An example of correctly detecting the left lane; (**b**) an example of failing to detect both lanes; (**c**) an example of correctly detecting the right lane; and (**d**) an example of correctly detecting both lanes.  
**Author Contributions:** Conceptualization, S.-H.L. ([PERSON]); methodology, S.-H.L. ([PERSON]) and S.-H.L. ([PERSON]); software, S.-H.L. ([PERSON]); validation, S.-H.L. ([PERSON]) and S.-H.L. ([PERSON]); formal analysis, S.-H.L. ([PERSON]) and S.-H.L. ([PERSON]); investigation, S.-H.L. ([PERSON]) and S.-H.L. ([PERSON]); resources, S.-H.L. ([PERSON]), S.-H.L. ([PERSON]) and H.-J.K.; data curation, S.-H.L. ([PERSON]), H.-J.K. and S.-H.L. ([PERSON]); writing--original draft preparation, [PERSON]-H.L. ([PERSON]); writing--review and editing, [PERSON]-H.L. ([PERSON]); visualization, S.-H.L. ([PERSON]); supervision, [PERSON]-H.L. ([PERSON]); project administration, [PERSON]-H.L. ([PERSON]); funding acquisition, [PERSON]-H.L. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Korea (NRF-2021R11A3049604), and supported by the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156389) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation).  
Not applicable.  
**Data Availability Statement:** BDD100k Dataset: [[https://doc.bdd100k.com/download.html](https://doc.bdd100k.com/download.html)]([https://doc.bdd100k.com/download.html](https://doc.bdd100k.com/download.html)) (accessed on 13 June 2023). All dataset is made freely available to academic and non-academic entities for non-commercial purposes.  
The authors declare no conflict of interest regarding the publication of this paper.