{
  "id": 18326,
  "url": "https://arxiv.org/abs/2608.08815v1",
  "title": "Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles",
  "summary": "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",
  "authors": "Pedram MohajerAnsari, Amir Salarpour, Mert D. Pesé",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-09T17:05:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18326",
  "original_url": "https://arxiv.org/abs/2608.08815v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}