Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08897 (cs)
[Submitted on 9 Mar 2026]
Title:Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures
Authors:David Fernandez, Pedram MohajerAnsari, Amir Salarpour, Long Cheng, Abolfazl Razi, Mert D. Pesé
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Abstract:Vision-language models are emerging for autonomous driving, yet their robustness to physical adversarial attacks remains unexplored. This paper presents a systematic framework for comparative adversarial evaluation across three VLM architectures: Dolphins, OmniDrive (Omni-L), and LeapVAD. Using black-box optimization with semantic homogenization for fair comparison, we evaluate physically realizable patch attacks in CARLA simulation. Results reveal severe vulnerabilities across all architectures, sustained multi-frame failures, and critical object detection degradation. Our analysis exposes distinct architectural vulnerability patterns, demonstrating that current VLM designs inadequately address adversarial threats in safety-critical autonomous driving applications.
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08897 [cs.CV] |
| (or arXiv:2603.08897v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08897
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View a PDF of the paper titled Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures, by David Fernandez and 5 other authors
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