发表机构
Alstom - Mobility Data Science and AI, Madrid, Spain; Alstom - Mobility Data Science and AI, Saint-Ouen, France; Alstom - Safety Engineering, Stockholm, Sweden; Alstom - Chief AI & Data Science Office, Pittsburgh, Pennsylvania USA（阿尔斯通 - 移动数据科学与人工智能，马德里，西班牙; 阿尔斯通 - 移动数据科学与人工智能，圣旺，法国; 阿尔斯通 - 安全工程，斯德哥尔摩，瑞典; 阿尔斯通 - 首席人工智能与数据科学办公室，匹兹堡，宾夕法尼亚州，美国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对铁路行业严格标准下AI应用受限的问题，提出通过鲁棒性、运行设计域和可解释性三个领域提升信任，以实现合规并推动AI在安全关键领域的广泛采用。
AI 中文摘要
人工智能（AI）目前仅应用于非安全关键领域，这是因为铁路行业有着严格的标准和法规。我们提议审视三个主要领域，这些领域对于提升对数据科学和AI算法的信任并实现合规至关重要：鲁棒性、运行设计域（ODD）和可解释性。鲁棒性是指AI系统在任何情况下保持其性能水平的能力（ISO24029）。ODD允许根据最近发布的DIN DKE SPEC 99004标准，明确定义系统预期运行的操作条件。可解释性是AI系统以人类能够理解的方式表达影响AI系统结果的重要因素的特性。这三个研究领域已被非铁路领域的参与者充分研究，其算法和方法已准备好用于铁路应用。需要采用系统视角，以确保所有可信性要求在安全的MLOps环境中持续交互，从而促进监管机构、运营商和公众的接受。除了保障安全关键应用外，我们旨在表明，正如全球监管框架现在所要求的那样，培养对AI的深度信任将释放其全部潜力，并改变关键任务领域中的采用速度。
英文摘要
Artificial Intelligence (AI) is currently only applied to non-safety critical applications due to the strict standards and regulations for railway industries. We propose to review the three main fields necessary to increase trust in data science and AI algorithms and reach compliance: robustness, Operational Design Domain (ODD), and explainability. Robustness is the ability of an AI system to maintain its level of performance under any circumstances (ISO24029). ODDs allow the explicit definition of operating conditions under which a system is intended to operate, according to the recently published DIN DKE SPEC 99004. Explainability is the property of an AI system to express important factors influencing the AI system results in a way that humans can understand. Those 3 domains of research are already well investigated by nonrailway actors, with algorithms and methods ready to use for railway applications. A system view is necessary to ensure all trustworthy requirements interact continuously in a safe MLOps environment thereby fostering acceptance from regulators, operators and the public. Beyond safeguarding safety-critical applications, we aim to show that fostering deep trust in AI, as now required by regulatory frameworks worldwide, will unlock its full potential and transform the pace of adoption across mission-critical domains.
CommentsTransport Research Arena 2026, accepted
