The Shift2Rail project RAILS addressed the topic “Artificial Intelligence for the railway sector”.
The project aimed to understand the potential of AI in railways, contribute to future research roadmaps, and address challenges associated with safety, dependability, security, and autonomy.
The main objectives included: identifying AI potential, aligning with ongoing railway innovation, recognizing necessary shifts, developing proofs-of-concept, creating benchmarks and simulations, outlining transition pathways, engaging relevant stakeholders, training young researchers.
To achieve these objectives, RAILS followed a Technology Road-Mapping Methodology, integrating research activities into comprehensive roadmaps for safety and automation (WP2), maintenance and inspection (WP3), and traffic planning and management (WP4).
The project assessed scientific, industrial, and regulatory landscapes, proposed pilot case studies, and outlined guidelines for applying AI approaches from other sectors.
Figure 1 provides a high-level view of the RAILS scope and research.
In conclusion, the roadmapping activities revealed that the railway industry is embracing AI but faces challenges such as the lack of standards, insufficient datasets, the need for innovative solutions like digital twins, testing methodologies rooted in mixed reality, and guidelines for the gradual introduction of AI in autonomous driving. The developed roadmaps provide insights and recommendations to guide future research, proposing innovative ideas, fostering advancements in railway technology, and facilitating consensus-building to bridge the gap between AI opportunities and their full exploitation in the railway sector.