# Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions

arXiv: https://arxiv.org/abs/2306.13290 · HTML: https://mlsanity.com/paper/2306.13290/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Clearing the Skies: A deep network architecture for single-image rain removal](https://mlsanity.com/paper/1609.02087/) (2016)
- [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://mlsanity.com/paper/2303.11040/) (2023)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
- [Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era](https://mlsanity.com/paper/2305.06131/) (2023)
- [Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model](https://mlsanity.com/paper/2304.11332/) (2023)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [DAWN: Vehicle Detection in Adverse Weather Nature Dataset](https://mlsanity.com/paper/2008.05402/) (2020)
- [Robustness of SAM: Segment Anything Under Corruptions and Beyond](https://mlsanity.com/paper/2306.07713/) (2023)
- [Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing](https://mlsanity.com/paper/2201.03246/) (2022)

## Built on by (most cited first, 14 of 14)

- [A Survey for Foundation Models in Autonomous Driving](https://mlsanity.com/paper/2402.01105/) (2024)
- [RobustSAM: Segment Anything Robustly on Degraded Images](https://mlsanity.com/paper/2406.09627/) (2024)
- [OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation](https://mlsanity.com/paper/2503.07098/) (2025)
- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images](https://mlsanity.com/paper/2409.13401/) (2024)
- [Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM](https://mlsanity.com/paper/2404.04996/) (2024)
- [LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion](https://mlsanity.com/paper/2507.02813/) (2025)
- [Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving](https://mlsanity.com/paper/2408.09839/) (2024)
- [Red-Teaming Segment Anything Model](https://mlsanity.com/paper/2404.02067/) (2024)
- [Segment Any-Quality Images with Generative Latent Space Enhancement](https://mlsanity.com/paper/2503.12507/) (2025)
- [RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditions](https://mlsanity.com/paper/2509.19165/) (2025)
- [3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data](https://mlsanity.com/paper/2401.06946/) (2024)
- [BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation](https://mlsanity.com/paper/2603.10828/) (2026)
- [Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement](https://mlsanity.com/paper/2601.02018/) (2026)

## Abstract (arXiv)

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.