Autonomous Tram (AT) systems are an emerging application requiring mission critical services to ensure safe and continuous operation. Key functions such as positioning, track occupancy detection, and obstacle perception depend on uninterrupted communication and mobility support. Leveraging the capabilities of 5G networks is thus crucial to support these functions. In this paper, we investigate the applicability of 5G Ultra-Reliable Low Latency Communications (URLLC) services for AT use case, with a particular focus on mobility challenges. We begin by conducting a comprehensive 5G coverage analysis in a real urban deployment to identify limitations in handover (HO) performance, which may compromise service reliability and, consequently, the accuracy and timeliness of positioning and perception functions. To address this, we propose a 5G Dual Connectivity Handover (DC HO), offering seamless transitions compared to classical 4G-based HO solutions. The proposed HO mechanism is validated through extensive simulations, comparing key performance metrics such as latency and reliability against classical 4G HO approach. Results demonstrate that the 5G DC HO strategy meets the stringent requirements of URLLC, thus enabling reliable support for AT operations in dynamic environments and safeguarding the critical services of positioning, track management, and environment perception.

Autonomous rail systems, including driverless trams, are gaining traction due to their potential to enhance efficiency, capacity, and operational cost-effectiveness. A central requirement for safe operation of autonomous tram vehicles is achieving ultra-reliable positioning accuracy, which traditionally relies on costly, infrastructure-heavy solutions like trackside beacons or GPS, alongside sensor fusion algorithms that often fail to meet the stringent requirements of Safety Integrity Level 4 (SIL4). These limitations create a significant barrier to the widespread adoption of autonomous light rail systems. This paper introduces the Consistency Check and Best performance Selection (CCBS) algorithm, a novel fully onboard solution that enhances data reliability for rail positioning and velocity estimation systems. Our method validates and combines outputs from multiple sensors, leveraging a sophisticated consistency check and a data performance selection mechanism to achieve a SIL4 level – even when individual data streams do not. This entirely onboard approach significantly improves positioning and velocity estimation accuracy and offers substantial cost and maintenance efficiencies by eliminating the need for trackside infrastructure.

Autonomous vehicles are nowadays gaining popularity in many different sectors, from automotive to aviation, and find application in increasingly complex and strategic contexts. In this domain, Obstacle Detection and Avoidance Systems (ODAS) are crucial and, since they are safety-critical systems, they must employ fault-detection and management techniques to maintain correct behavior. One of the most popular techniques to obtain a reliable system is the use of redundancy, both at the hardware and at the software levels. With the objective of improving fault-detection while producing little impact on the programmability of the system, this paper introduces a general and lightweight monitoring technique based on a user-directed observer design pattern, which aims at monitoring the validity of predicates over state variables of the algorithms in execution. This can increase the fault-detection capability and even anticipate the detection time of some faults that would be caught by replication only at later times. Results are evaluated on a real-world use-case from the railway domain, and show how the proposed fault-detection mechanism can increase the overall reliability of the system by up to 24.4% compared to replication alone in case of crowded scenarios over the entire tracking process, and up to 43.9% in specific phases.

Beyond 5G (B5G) communication networks face the challenge of meeting the demanding requirements of various service types, including uRLLC, mIoT, eMBB, and emerging technologies like Extended Reality (XR). Edge computing can address these demands effectively because of the ability to bring computational power and resources closer to the source of data. Nevertheless, the realization of this potential necessitates an open, flexible, and automated architectural framework capable of supporting disaggregated applications and network designs. In this context, this paper introduces a novel architecture designed to advance the evolution of edge computing in B5G, developed within the EU-funded project VERGE. The proposed architecture is modular and scalable, guided by artificial intelligence (AI), and founded on three essential pillars: “edge for AI,“ “AI for edge,“ and “security, privacy, and trustworthiness for AI.“ After presenting this architecture, the paper showcases its applicability through the examination of two vertical use cases within the industrial and transportation domains.

The substitution of traditional occupancy detecting sensors with an Autonomous Positioning System (APS) is a promising solution to contain costs and improve performance of current tramway signalling systems. APS is an onboard system using satellite positioning and other inertial platforms to autonomously estimate the position of the tram with the needed levels of uncertainty and protection. However, autonomous positioning introduces, even in absence of faults, a quantitative uncertainty with respect to traditional sensors. This paper investigates this issue in the context of an industrial project: a model of the envisaged solution is proposed, and it is analysed using Uppaal Statistical Model Checker. A novel model-driven hazard analysis approach to the exploration of emerging hazards is proposed. The analysis emphasises how the virtualisation of legacy track circuits and on-board satellite positioning equipment may give rise to new hazards, not present in the traditional system.

In this paper, we propose a Radio Access Network (RAN) slicing mechanism for mission-critical services in the context of location-aware Vehicle to Infrastructure (V2I) communications. In particular, the available location information is used to design an ad-hoc wireless network with both inter and intra-slice radio isolation obtained through the reservation of bandwidth (BW) resources. In this general setting, a particular emphasis is given to the use case of autonomous driving for Lightrails and Tramways systems. We assess the effectiveness of the proposed mechanism for guaranteeing isolation and for providing the required Quality of Service (QoS) through simulations. In order to evaluate realistic and reference scenario settings, we leverage a customized version of the system-level 5G-air-simulator.

Traditional solutions for tramway interlocking systems are based on physical sensors (balizes) distributed along the infrastructure which detect passing of the trams and trigger different actions, like the communications with the ground infrastructure and the interlocking system. This approach is not easily scalable and maintainable, and it is costly. The SISTER project designed new architectural solutions for addressing the previous problems based on the virtualization of the sensors and on the local positioning of each tram. The key idea is to trigger actions when the computed local position corresponds to a virtual tag. However, the computed position can be affected by errors, compared to the real one. Therefore, it is important to understand the impact of these new solutions on the traffic that can be supported by the tramway network. This paper presents a stochastic modeling approach for analysing the performability of a tramway system based on the SISTER architectural solutions, aiming to identify the parts of the tramway network that are more critical and sensible to the variation of the traffic conditions and to the setting of the key architectural parameters. We build a model using Stochastic Activity Networks and run sensitivity analyses on (i) the accuracy of the positioning, (ii) the different SISTER parameters, and (iii) considering possible outages temporarily blocking the journey of a tram. This analysis allows to properly set and fine-tune the key architectural parameters, to understand the impact of the accuracy on the positioning, to understand the impact of the outages.

The high-performance requirements needed to implement the most advanced functionalities of current and future Cyber-Physical Systems (CPSs) are challenging the development processes of CPSs. On one side, CPSs rely on model-driven engineering (MDE) to satisfy the non-functional constraints and to ensure a smooth and safe integration of new features. On the other side, the use of complex parallel and heterogeneous embedded processor architectures becomes mandatory to cope with the performance requirements. In this regard, parallel programming models, such as OpenMP or CUDA, are a fundamental brick to fully exploit the performance capabilities of these architectures. However, parallel programming models are not compatible with current MDE approaches, creating a gap between the MDE used to develop CPSs and the parallel programming models supported by novel and future embedded platforms.The AMPERE project will bridge this gap by implementing a novel software architecture for the development of advanced CPSs. To do so, the proposed software architecture will be capable of capturing the definition of the components and communications described in the MDE framework, together with the non-functional properties, and transform it into key parallel constructs present in current parallel models, which may require extensions. These features will allow for making an efficient use of underlying parallel and heterogeneous architectures, while ensuring compliance with non-functional requirements, including those on real-time performance of the system.

The research project SISTER aims to improve the safety and autonomy of light rail trains by developing and integrating novel technologies for remote sensing and object detection, safe positioning, and broadband radio communication. To prove safety of the SISTER solution, CENELEC-compliant Verification and Validation (V&V) is obviously required. In the SISTER project, we tackled the challenge of defining and applying a compact V&V methodology, able to provide convincing safety evidence on the solution, but still within the reduced resources available for the project. A relevant characteristic of the methodology is to produce V&V results that can be reused for future industrial exploitation of SISTER outcomes after project termination. This paper presents the V&V methodology that is currently applied in parallel to the progress of project activities, with preliminary results from its application.

One promising option to improve performance and contain costs of current tramway signalling systems is to introduce an Autonomous Positioning System (APS) in substitution of traditional occupancy detecting sensors. APS is an onboard system that uses a plurality of sensors (such as GPS or inertial platform) and a Sensor Fusion Algorithm (SFA) to autonomously estimate the position of the tram with the needed levels of uncertainty and protection. Autonomous positioning however introduces, even in absence of faults, a quantitative uncertainty with respect to traditional sensors. This paper investigates this issue in the context of an industrial project: a model of the envisaged solution is adopted, and the Uppaal Statistical Model Checker is used to study possible hazards induced by the substitution of legacy track circuits with on-board satellite positioning equipment.

Current solutions for tramway Interlocking Systems are based on physical sensors (balizes) distributed along the infrastructure which detect passing of the trams and trigger different actions. This approach is not easily scalable and maintainable, and it is costly. The Regional Project SISTER aims at designing new architectural solutions for addressing the previous problems based on the virtualization of the sensors and on the local positioning of each tram. The idea is to trigger actions when the computed local position corresponds to a virtual tag. However, the computed position can be affected by errors, compared to the real one. Therefore, it is important to understand the impact of these new solutions on the performability of the system. This paper focuses on the analysis of the performability of a tramway system, based on the SISTER architectural solutions. We build a model using Stochastic Activity Networks and run sensitivity analyses on i) the accuracy of the positioning, ii) the different SISTER parameters (e.g., those triggering the activation of manual procedures). This analysis allows us to properly set and fine-tune the key architectural parameters, to understand the impact of the accuracy on the positioning, and to understand the impact of failures.

This paper deals with the performance of LTE-A (Long Term Evolution-Advanced) cellular networks for supporting operational, safety-critical signaling, and standard IT services for urban transportation. Our interests have been focused on Light Rail Transit (LRT) signaling performance for the Songjiang (China) tramway project. Several stationary and mobility use cases have been considered and mean end-to-end delay and Packet Loss Rate (PLR) key performance indicators have been evaluated. Simulation results highlight that, in stationary conditions, LRT signaling performance requirements are fulfilled, while PLR performance degrades when mobility is introduced. Furthermore, we have evaluated the impact of non-critical IT traffic (e.g., UDP-based video) on TCP-based signaling by demonstrating that signaling throughput is not affected by video in stationary scenarios, whereas, in the presence of mobility, handovers degrade signaling performance, which can be guaranteed only if a QoS-aware scheduler is adopted. In conclusions, our results demonstrate that LTE-A can safely support operative and non-critical applications in urban transportation scenarios, where strong connectivity requirements are crucial.

The LRT (Light Rail Transit) systems are a kind of urban transport that has aspects in common to both tramways and metros. This paper analyses the Thales LRT On-Board-Systems (OBS) architecture, which is designed to achieve a high level of availability. Such architecture is built on top of open source technologies and consolidated telecommunication standards. Architectural requirements are met also thanks to the used Open-Source foundations. In particular the Qt framework, the 0MQ and the ASN.1 to C compiler have been used to develop a micro-service oriented fault resistant system. Redundant services are spawned on replicated identical hardware units, one of which is the master, and are seamlessly and automatically kept in sync by the algorithms described in this paper. In case of a service failure on one of the replicated hardware boxes, a choice is made between two alternatives: (1) a full mastership changeover is performed and another redundant box becomes the new master (2) a micro-service is migrated to another redundant box in order to take control of the same non-faulty device. The described architecture is being actively used in both LRT and metro solutions, thus this work will describe the benefits on the field and the effectiveness of the architecture in terms of code quality and maintainability. Since the development of the mentioned projects has been carried on inside an Agile team, some considerations will be made about benefits, constraints and pitfalls of such kind of methodologies, on strictly regulated and safety related projects.