Project manager at Linnaeus University
Linnaeus University; Consorzio Interuniversitario Nazionale per l'Informatica (CINI), Italy; Delft University of Technology, the Netherlands; University of Leeds, England
EU Horizon 2020 – Shift2Rail Joint Undertaking Call number S2R-OC-IPX-01-2019
1 Dec 2019–30 Nov 2022
Computer science (Department of Computer Science and Media Technology, Faculty of Technology)
More about the project
The overall objective of the Roadmaps for AI integration in the raiL Sector (RAILS) research project is to investigate the potential of artificial intelligence (AI) approaches in the rail sector, and contribute to the definition of roadmaps for future research in next generation signalling systems, operational intelligence, and network management. RAILS will address the training of doctoral students to support the research capacity in AI within the rail sector across Europe by involving research institutions with a combined background in both computer science and transportation systems, in four different countries: Italy, United Kingdom, the Netherlands, and Sweden.
RAILS will produce knowledge, ground breaking research and experimental proof-of-concepts for the adoption of AI in rail automation, predictive maintenance and defect detection, traffic planning, and capacity optimization. As such, RAILS will effectively contribute to the design and implementation of smarter railways. To that aim, RAILS will combine AI paradigms like machine learning with the Internet of Things (IoT), in order to leverage on the big amount of data generated by smart sensors and applications. The research activities will be conducted in continuity with ongoing research in railways, in particular within the Shift2Rail innovation program, and will be based on in-depth analysis of AI applications in transport and other relevant sectors, in order to perform a transferability study of available results to railways.
The methodological and technological concepts developed in RAILS are expected to stimulate further innovation in railways, providing new research directions to improve reliability, maintainability, safety, security, and performance. With respect to safety related aspects, emerging threats (e.g. the so-called adversarial attacks) and certification issues will be addressed when adopting AI in autonomous and cooperative driving (e.g. virtual coupling), based on the concepts of explainable AI (XAI) and trustworthy AI.
With respect to cyber-physical threat detection, innovative approaches will be developed based on AI models like Artificial Neural Networks (ANN) and Bayesian Networks together with multi-sensor data fusion and artificial vision. Resilience and optimization techniques based on genetic algorithms and self-healing will be addressed to face failures and service disruptions, as well as to increase efficiency and line capacity. All those techniques will pave the way to the development of the new Railway 4.0.
The project is part of the research in the Cyber-Physical Systems research group.