In the design, development, and operation of a rail transport system, all the actors involved use one or more safety methods to identify hazardous situations, the causes of hazards, potential accidents, and the severity of the consequences that would result. The main objective is to justify and ensure that the design architecture of the transportation system is safe and presents no particular risk to users or the environment. As part of this process of certification, domain experts are responsible for reviewing the safety of the system, and are being brought in to imagine new scenarios of potential accidents to ensure the exhaustiveness of such safety studies. One of the difficulties in this process is to determine abnormal scenarios that could lead to a particular potential accident. This is the fundamental point that motivated the present work, whose objective is to develop tools to assist certification experts in their crucial task of analyzing and evaluating railway safety. However, the type of reasoning (inductive, deductive, by analogy, etc.) used by certification experts as well as the very nature of the knowledge manipulated in this certification process (symbolic, subjective, evolutionary, empirical, etc.) justify that conventional computer solutions cannot be adopted; the use of artificial intelligence (AI) methods and techniques helps to understand the problem of safety analysis and certification of high-risk systems such as guided rail transport systems. To help experts in this complex process of evaluating safety studies, we decided to use AI techniques and in particular machine learning to systematize, streamline, and strengthen conventional approaches used for safety analysis and certification.

中文翻译：

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人工智能对铁路事故风险评估的贡献

在铁路运输系统的设计，开发和运行中，所有参与人员都使用一种或多种安全方法来识别危险情况，危险原因，潜在事故以及可能造成的后果的严重性。主要目的是证明并确保运输系统的设计架构安全，并且不会对用户或环境造成任何特殊风险。作为此认证过程的一部分，领域专家将负责检查系统的安全性，并被带入想象中潜在事故的新场景，以确保此类安全研究的详尽无遗。此过程中的困难之一是确定可能导致特定潜在事故的异常情况。这是推动当前工作的基础，其目的是开发工具以协助认证专家完成分析和评估铁路安全的关键任务。但是，认证专家使用的推理类型（归纳，演绎，类推等）以及在此认证过程中操纵的知识的本质（符号，主观，进化，经验等）证明了传统不能采用计算机解决方案；人工智能（AI）方法和技术的使用有助于了解安全分析和高风险系统（如导轨运输系统）认证的问题。为了帮助专家评估安全研究这一复杂的过程，我们决定使用AI技术，尤其是机器学习来系统化，简化，