发表机构
University of Padova; Örebro University; Fondazione Bruno Kessler（帕多瓦大学; 厄勒布鲁大学; 布鲁诺·凯斯勒基金会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了移动出行中LLM的应用与安全挑战，发现研究集中于GPT和Llama模型及交通应用，但忽视安全、隐私和可靠性，且AI法案合规性差，强调需安全设计。
AI 中文摘要
移动出行领域正经历着由生成式人工智能进步驱动的范式转变。仅考虑汽车，全球市场年价值约为2.9万亿美元，这些技术的整合有可能影响全球超过15亿辆汽车。随着大型语言模型（LLMs）在移动出行中的日益采用，网络安全、隐私和可靠性方面的担忧随之出现。因此，本文调查了当前应用并评估了这些挑战。由于欧洲人工智能法案将交通人工智能归类为高风险，我们从其要求中推导出九个技术类别，以评估当前研究和未来部署。我们的研究结果表明，研究主要关注GPT和Llama模型（占所审查工作的50%以上）以及交通应用，而在很大程度上忽视了安全性、隐私和可靠性。这一差距延伸至人工智能法案合规性：在审查的35项工作中，只有一项包含部分漏洞评估，一项包含部分风险管理体系。我们识别出强优化性能与法规遵从之间的明显差距，表明合规性受限于对静态性能而非生命周期安全的关注，而非技术本身，并强调在安全关键型智能交通系统中迫切需要安全设计。
英文摘要
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has the potential to impact more than 1.5 billion vehicles worldwide. As Large Language Models (LLMs) are increasingly adopted in mobility, concerns about cybersecurity, privacy, and reliability emerge. Accordingly, this paper surveys current applications and assesses these challenges. Since the European AI Act classifies transportation AI as high risk, we derive nine technical classes from its requirements to assess current research and future deployments. Our findings show that research mainly studies GPT and Llama models (over 50\% of reviewed works) and traffic applications while largely neglecting security, privacy, and reliability. This gap extends to AI Act compliance: among 35 reviewed works, only one includes a partial vulnerability assessment and one a partial risk management system. We identify a clear gap between strong optimization performance and regulatory adherence, suggesting compliance is limited less by technology than by a focus on static performance over lifecycle safety, and underscoring an urgent need for security-by-design in safety-critical intelligent transportation systems.
