No abstract

The progressive adop?on of ar?ficial intelligence and advanced communica?on technologies within railway control and automa?on has brought up a huge poten?al in terms of op?misa?on, learning and adapta?on, due to the so-called "self-x" capabili?es; however, it has also raised several dependability concerns due to the lack of measurable trust that is needed for cer?fica?on purposes. In this paper, we provide a vision of future train control that builds upon exis?ng automa?c train opera?on, protec?on, and supervision paradigms. We will define the basic concepts for autonomous driving in digital railways, and summarise its feasibility in terms of challenges and opportuni?es, including explainability, autonomic compu?ng, and digital twins. Due to the clear architectural dis?nc?on, automa?c train protec?on can act as a safety envelope for intelligent opera?on to op?mise energy, comfort, and capacity, while intelligent protec?on based on signal recogni?on and obstacle detec?on can improve safety through advanced driving assistance.

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

In the last years, there has been a growing interest in the emerging concept of Digital Twins (DTs) among soJware engineers and researchers. DTs represent a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems. They can play a key role in different domains, as it is also witnessed by several ongoing standardisa2on ac2vi2es. However, several challenging issues have to be faced in order to effec2vely adopt DTs, in par2cular when dealing with cri2cal systems. This work provides a review of the scien2fic literature on DTs in the railway sector, with a special focus on their rela2onship with Ar2ficial Intelligence. Challenges and opportuni2es for the usage of DTs in railways have been iden2fied, with interoperability being the most discussed challenge. One difficulty is to transmit opera2onal data in real-2me from edge systems to the cloud in order to achieve 2mely decision making. We also provide some guidelines to support the design of DTs with a focus on machine learning for railway maintenance.