Jeong, S.; Ko, J.; Lee, S.; Kang, J.; Kim, Y.; Park, S.Y.; Mun, S. Optimizing Lane Departure Warning System Towards AI-Centered Autonomous Vehicles. Sensors2024, 24, 2505.
Jeong, S.; Ko, J.; Lee, S.; Kang, J.; Kim, Y.; Park, S.Y.; Mun, S. Optimizing Lane Departure Warning System Towards AI-Centered Autonomous Vehicles. Sensors 2024, 24, 2505.
Jeong, S.; Ko, J.; Lee, S.; Kang, J.; Kim, Y.; Park, S.Y.; Mun, S. Optimizing Lane Departure Warning System Towards AI-Centered Autonomous Vehicles. Sensors2024, 24, 2505.
Jeong, S.; Ko, J.; Lee, S.; Kang, J.; Kim, Y.; Park, S.Y.; Mun, S. Optimizing Lane Departure Warning System Towards AI-Centered Autonomous Vehicles. Sensors 2024, 24, 2505.
Abstract
Autonomous vehicle technology, employing image sensors for road marking recognition, is largely dependent on the Lane Departure Warning System (LDWS) to prevent vehicular deviations from driving lanes. The effectiveness of image-based lane recognition, however, is susceptible to environmental influences like weather conditions and the surrounding road environment. This study investigates the impact of road marking retro-reflectivity on LDWS under varied environmental conditions. Conducted at the Yeoncheon SOC Demonstration Research Center, the experiments encompassed a spectrum of weather scenarios, incorporating rainfall and shifts between day and night lighting. Controlled wear was applied to white, yellow, and blue road markings, followed by measuring their retro-reflectivity at different stages of degradation. The LDWS's performance was evaluated by assessing the recognition rates of these markings under these diverse environmental settings. The study reveals that enhanced retro-reflectivity significantly boosts LDWS detection, especially in challenging weather conditions. Furthermore, the research led to the development of a simulation framework to analyze the cost-effectiveness of road marking maintenance, aligning it with the safety needs of autonomous vehicles. This framework suggests the necessity for updated road marking guidelines that cater to the advanced needs of driver assistance systems and autonomous vehicle technology.
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