Impact of Environmental Conditions on YOLOv8-Based Traffic Sign Detection: A Controlled Comparison of Simulated and Real Weather Cases.

Robust traffic sign detection is essential for reliable autonomous driving systems; however, detection performance can be substantially affected by adverse environmental conditions. Although simulation-based approaches are widely used to evaluate robustness, the extent to which simulated weather reproduces real-world environmental effects remains insufficiently understood. This study presents a controlled, comparative evaluation methodology for assessing a YOLOv8-based traffic sign detection model across four environmental conditions: clear (sunny), simulated rain, real-world rain, and simulated snow. The same road segment, camera configuration, and predefined set of 55 traffic-sign instances were maintained across the evaluated conditions, enabling interpretable comparisons while minimizing scene-level variability. Detection performance was assessed using representative detection confidence (RDC) at the traffic-sign-instance level, along with image-quality metrics such as sharpness, saturation, and intensity. Mean RDC was highest under clear conditions (0.825), followed descriptively by simulated snow (0.647), real-world rain (0.408), and simulated rain (0.395). However, simulated and real-world rain did not differ significantly in RDC (Holm-adjusted