In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing/actuation infrastructure and the AI algorithms’ results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships’ navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.

In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds on itself. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. The railway sector offers many aspects which can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly.

A distributed decision support system has been developed to assist seafarers during several navigation tasks, for instance, in avoiding a collision with a detected obstacle in the sea and envisioning a future autonomous navigation system. In this paper, the decision support system is based on the results of a customized simulation model representing the ship’s behavior, including hydrodynamics, propulsion, and control effects. Sensors monitor and collect the parameters of the environment and the ship onboard. The telemetry and the calculated route are visualized on a wearable visor exploiting augmented reality. Such context information is also replicated ashore through a narrow-band satellite link using an IoT publish-subscribe communication paradigm to allow one or more remote seafarers to supervise the situation in a virtual reality environment. Overall, the potential of the proposed system is presented and discussed for application in the context of autonomous navigation.

The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must limit accidents. This paper presents the human-centered outcomes of the VRAIL project, an industrial research project aiming to use enabling technologies and develop methodologies for operators directly involved in infrastructure management in the railway field. Developing integrated monitoring systems and applications that exploit Augmented Reality (AR) and Virtual Reality (VR) becomes crucial to support the awareness of planning and maintenance operators required to comply with high-quality standards. This paper addresses the abovementioned issue by proposing the development of two different prototype applications in both AR and VR for railway infrastructure data management. These environments will provide the planning operator with a complete platform to explore, use to plan maintenance interventions, and gather detailed reports to improve the overall safety of the railway line effectively.

This paper contributes to the open innovation literature by presenting a successful application of the open innovation approach in the maritime sector between Fincantieri (FC), one of the world’s largest shipbuilding groups, and the National Research Council of Italy (CNR, Consiglio Nazionale delle Ricerche), the largest research entity in Italy. By using external sources of scientific innovation, specifically 10 different groups of researchers from CNR research institutes and three universities, and by integrating these groups with a team of expert Fincantieri’s designers, the company obtained significant advances in terms of technological content and competitiveness. This collaboration is an evident example of successful implementation of the open innovation paradigm, where a big company (Fincantieri) uses external sources of innovation (the CNR researchers) to advance its technology by carrying out six different projects simultaneously. The paper presents the adopted open innovation model, the governance approach specifically implemented by the company and the major scientific contents and outcomes of the constellation of the six connected projects.

XR technologies enhance the sustainable development of urban areas by merging digital and physical worlds. In smart city contexts, XR has been applied in miscellaneous ways, from supporting urban planning and design through immersive visualization, to improving traffic and navigation services via real-time overlays, and to enhancing public safety and emergency response through simulation and situational support. However, the literature does not clearly categorize XR application domains in smart cities, interaction methods, and types of sensory feedback. This study presents an SLR reported in accordance with the PRISMA 2020 guidelines. We included 92 studies published between 2009 and 2024, proposing a classification of application domains, interaction modalities, and sensory feedback. We searched Scopus, Web of Science, and IEEE Xplore using predefined search terms and eligibility criteria. This review offers a comprehensive overview of nearly 20 years of XR research in smart cities, highlighting established practices and guiding future application development and research directions.