Enhancing Self-Driving Segmentation in Adverse Weather Conditions: A Dual Uncertainty-Aware Training Approach to SAM Optimization

Recent advancements in vision foundation models, such as the Segment Anything Model (SAM) and its successor SAM2, have established new state-of-the-art benchmarks for image segmentation tasks. However, these models often fail in inclement weather scenarios where visual ambiguity is prevalent, primarily due to their lack of uncertainty quantification capabilities. Drawing inspiration from recent successes in medical imaging—where uncertainty-aware training has shown considerable promise in handling ambiguous cases—we explore two approaches to enhance segmentation performance in adverse driving conditions. First, we implement a multistep fine-tuning process for SAM2 that incorporates uncertainty metrics directly into the loss function to improve overall scene recognition. Second, we adapt the Uncertainty-Aware Adapter (UAT), originally developed for medical image segmentation, to autonomous driving contexts. We evaluate these approaches on the CamVid and BDD100K datasets, while the GTA Driving dataset is used exclusively during the fine-tuning process for adaptation and not for evaluation, helping improve generalization to diverse driving conditions. Our experimental results demonstrate that UAT-SAM improves IoU by 42.7% and Dice by 30% under heavy-weather conditions, while the fine-tuned SAM2 with uncertainty-aware loss shows improved performance across a wide range of driving scenes. These findings highlight the importance of explicit uncertainty modeling in safety-critical autonomous driving applications, particularly when operating in challenging environmental conditions.