mlsanityData to 14 Oct 2026

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

arXiv 2306.13290 ↗First posted 23 Jun 2023cs.CVcited by 14

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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.

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

23 of 29 references.

References · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023Segment Anythingcited by 8,234
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2013Intriguing properties of neural networkscited by 5,155
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  7. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  8. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  9. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  10. 2016Adversarial Machine Learning at Scalecited by 936
  11. 2021Florence: A New Foundation Model for Computer Visioncited by 804
  12. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 265
11 more
  1. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  2. 2016Clearing the Skies: A deep network architecture for single-image rain removalcited by 136
  3. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  4. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  5. 2023A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AIcited by 68
  6. 2023Generative AI meets 3D: A Survey on Text-to-3D in AIGC Eracited by 61
  7. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  8. 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
  9. 2020DAWN: Vehicle Detection in Adverse Weather Nature Datasetcited by 41
  10. 2023Robustness of SAM: Segment Anything Under Corruptions and Beyondcited by 31
  11. 2022Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 4; 2026: 2.

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  1. 2024A Survey for Foundation Models in Autonomous Drivingcited by 52
  2. 2024RobustSAM: Segment Anything Robustly on Degraded Imagescited by 36
  3. 2025OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentationcited by 28
  4. 2024Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunitiescited by 24
  5. 2024PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Imagescited by 24
  6. 2024Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAMcited by 23
  7. 2025LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusioncited by 6
  8. 2024Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Drivingcited by 5
  9. 2024Red-Teaming Segment Anything Modelcited by 4
  10. 2025Segment Any-Quality Images with Generative Latent Space Enhancementcited by 3
  11. 2025RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditionscited by 2
  12. 20243D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Datacited by 1
2 more
  1. 2026BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentationcited by 1
  2. 2026Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancementcited by 0