Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles

Traffic sign recognition (TSR) models based on deep neural networks achieve strong clean-data performance but remain vulnerable to physically realizable adversarial attacks, including shadow perturbations, natural-light interference, and printed patches. Existing defenses often improve robustness against one attack type while degrading performance on others, and can reduce clean accuracy. We propose LAMDA (Language-Anchored Model for Direction Alignment), a training framework that transfers lang

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ethics.ai (9 August 2026), “Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles,” evidence record 18326, https://ethics.ai/record/18326 (originally published by arXiv cs.LG).

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