An Ultralytics YOLOv8-Based Approach for Road Detection in Snowy Environments in the Arctic Region of Norway

In recent years, advancements in autonomous vehicle technology have accelerated, promising safer and more efficient transportation systems. However, achieving fully autonomous driving in challenging weather conditions, particularly in snowy environments, remains a challenge. Snow-covered roads introduce unpredictable surface conditions, occlusions, and reduced visibility, that require robust and adaptive path detection algorithms. This paper presents an enhanced road detection framework for snowy environments, leveraging Simple Framework for Contrastive Learning of Visual Representations (SimCLR) for Self-Supervised pretraining, hyperparameter optimization, and uncertainty-aware object detection to improve the performance of You Only Look Once version 8 (YOLOv8). The model is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø, Norway, which covers a range of snow textures, illumination conditions, and road geometries. The proposed framework achieves scores in terms of mAP@50 equal to 99% and mAP@50–95 equal to 97%, demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions. The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments, enabling robust decision-making in hazardous weather conditions. This research lays the groundwork for more resilient perception models in self-driving systems, paving the way for the future development of intelligent and adaptive transportation networks.