Segment Anything Model (SAM) has gained considerable interest in recent times for its remarkable performance and has emerged as a foundational model in computer vision. It has been integrated in diverse downstream tasks, showcasing its strong zero-shot transfer capabilities. Given its impressive performance, there is a strong desire to apply SAM in autonomous driving to improve the performance of vision tasks, particularly in challenging scenarios such as driving under adverse weather conditions. However, its robustness under adverse weather conditions remains uncertain. In this work, we investigate the application of SAM in autonomous driving and specifically explore its robustness under adverse weather conditions. Overall, this work aims to enhance understanding of SAM's robustness in challenging scenarios before integrating it into autonomous driving vision tasks, providing valuable insights for future applications.

翻译：Segment Anything Model（SAM）近期因卓越性能引发广泛关注，已成为计算机视觉领域的奠基性模型。该模型已集成至多种下游任务中，展现出强大的零样本迁移能力。鉴于其出色表现，研究者迫切希望将SAM应用于自主驾驶领域，以提升视觉任务性能，特别是在恶劣天气条件下的驾驶等挑战性场景中的表现。然而，SAM在恶劣天气条件下的鲁棒性仍存在不确定性。本研究探讨SAM在自主驾驶中的应用，并重点分析其在恶劣天气条件下的鲁棒性。总体而言，本工作旨在将SAM集成至自主驾驶视觉任务前，增强对其在挑战性场景中鲁棒性的理解，为未来应用提供宝贵见解。