Robustness of Segment Anything Model (SAM) for Autonomous Driving in
  Adverse Weather Conditions
Xinru Shan Chaoning Zhang
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.