Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles

TL;DR

This study shows that road marking visibility affects lane departure warning systems in autonomous vehicles, especially in bad weather, and suggests updating road marking standards for better safety.

Contribution

The study introduces a simulation framework to evaluate the cost-effectiveness of road marking maintenance for autonomous vehicle safety.

Findings

Higher retro-reflectivity improves LDWS detection, especially in adverse weather.

A simulation framework was developed to analyze road marking maintenance cost-effectiveness.

Current road marking guidelines need revision to meet autonomous vehicle sensor requirements.

Abstract

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.

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