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The Rail and Road transportation systems are evolving to progressively introduce automation with the ultimate goal of autonomous driving. The expectations are to improve efficiency and to guarantee higher level of safety. As transport networks will evolve towards high mobility, vehicles will become connected and self-positioned opening new challenges to comply with stringent safety requirements. Although vehicles are increasingly equipped with multiple advanced onboard positioning sensors, GNSS corrections allow to minimize the confidence error and to standardize the on board units. In the ERTMS (European Railways Train Management System) GNSS positioning must comply with the Tolerable Hazard Rate of 10E-9/hour at system level. Similar requirements are targeted by the connected cars when the management of vehicles is performed by a centralized authority as in the ERTMS system. Both ERTMS and Connected Car applications rely on the knowledge of the vehicle’s position with similar integrity levels. Considering also that rail and roads are frequently close to each other’s sharing the same operational environment, a multimodal augmentation network has been designed in the HELMET project to satisfy the new market needs. The aim of this paper is to assess the technical feasibility and the economic sustainability of this multi-modal augmentation network designed for the rail and road applications. The prerequisites are to reuse existing and future EGNSS infrastructures - without creating a direct dependence on themselves - to guarantee the interoperability and to allow a stepped deployment in line with the plans of the rail and road infrastructure managers. The paper includes the results of a recently concluded field test and the requirements of RFI – the Italian railways infrastructure manager who is managing a working group with rail industries to contributing to a standardizable and interoperable GNSS-based ERTMS system. Follow up activities to consolidate results for an operational system are on-going.

Recently, multi-sensor localization strategies are gaining attention in the railway scenario. In fact, the current trend is to reduce or remove the physical equipment deployed along the track for positioning purposes and to exploit on-board sensors to realize the same functionalities. Although GNSS is one of the major resources to perform this task, its performances dramatically decrease in presence of sources of local hazards like multipath, shadowing and blockage. For this reason, multi-sensor positioning methods are under study. Among them, those based on the detection of landmarks constituted by georeferenced trackside infrastructure elements like rail signs, and the estimation of the relative position of the train with respect to them are rather promising. Thus, in this paper we focus on the construction of the section of a Rail Digital Map related to these infrastructure elements on the basis of the fusion of the outputs of a stereo video camera and a LIDAR. In particular, the algorithms for object detection, single epoch landmark position estimation and landmark tracking are discussed. Results of the performance assessment based on Monte Carlo simulations are also reported.

L'article présente le projet ERSAT GGC et les techniques de détection des effets locaux sur les GNSS développées pour l'introduction du GNSS dans le système ERTMS/ETCS

In this paper we present a modular framework to classify railway track areas regarding the expected presence of local GNSS threats. This information might be critical for a safe signalling operation, for example to determine where virtual balises could be placed safely. We show first how different GNSS threats can be detected using dedicated detection algorithms and how these individual detection results can be then transformed from time to the track domain. An overall decision logic is subsequently used to identify an area as suitable or unsuitable for GNSS usage by combining all available GNSS data collected over the same track area. Finally, the framework implementation is evaluated with railway data obtained during a measurement campaign in Sardinia, Italy in 2019. Even though developed in the railway context, the presented framework architecture and methodology may be also considered to perform similar classification tasks for other means of transport.

Satellite navigation plays a critical role in new railway signaling and train control systems. It will allow higher levels of automation at a reduce cost in installation and maintenance as compared to current infrastructure-based technologies. However, in order to be integrated in railway systems, GNSS must guarantee the stringent railway safety requirements. GNSS safety has been extensively quantified for the aviation community, however, in the railway environment the probability of local GNSS hazards like interferences is larger due to its operation in a non-restricted space. Railway GNSS receivers must be therefore equipped with mechanisms to protect the position determination from being corrupted by undesired interferences (jamming or spoofing) without being aware of it. In this paper and based on Commercial-Off-The-Shelf (COTS) hardware we implemented interference detection functions suitable for railway localization. We detail the algorithm design as well as the necessary calibration procedure to determine the nominal signal model. The performance of the interference detection is analyzed with an experimental setup on a commercial train during the measurement campaign organized for the EU project ERSAT-GGC. Figures about the detection capability and detected events along the selected line are finally provided.

This deliverable is the second report delivered by GATE4Rail WP3. The general objective of WP3 was to define a comprehensive methodology and the associated tools able to characterise the Global Navigation Satellite System (GNSS) performance into the railways application scenarios defined in WP2 in the framework of a VB detection in ERTMS (European Railway Traffic Management System)/ETCS (European Train Control System) context.The work performed in WP3 is focused on the definition of the main railway environments that will impact GNSS reception, thus PVT performance. This deliverable D3.2 in particular, defines which are the key hazards (both local and global) that have to be taken into account and simulated in order to cover the main events that can occur during a railway operational scenario and how error models will be introduced in the simulation framework defined by the project.

Before deployment and commissioning of new positioning products/applications, manufacturers need to perform long and often expensive field trials in targeted operational environments in order to assess and certify future behaviour. The community trust on GNSS (Global Navigation Satellite System) is so high that more and more safety critical applications based on satellite navigation are under development. With the increase of criticality level of GNSS applications, a relevant means to predict the performances and reliability of future applications is needed. Thus in this paper we introduce a new architecture based on Machine Learning that combines classical observables for local hazard detection, with the outcome of advanced RAIM in order to determine whether a given point of a railway is suitable for a safe and reliable use of GNSS for train positioning.

This document is the deliverable D4.2-Technical Specification of the Survey Toolset where the outcome of the work performed in WP4.2 of the ERSAT GGC project is described. This document follows up the work performed in WP4.1 and reported in the deliverable D4.1, extending the ongoing general definition of the track area classification process. The goal of WP4.2 is the specification and development of the toolset for the survey and classification of track areas, giving the system overview and providing the different interacting blocks as the measurement hardware system, the processing software toolbox and the final classification data. Regarding this D4.2, the functional and technical specifications of the toolset are here provided with the description of the interfaces between the different detection techniques and decision logic blocks. Outcome of D4.3 will be the prototype implementation and installation. The document also includes the specification of the measurement system and hardware to be used in the survey process so that the necessary data for the classification techniques can be collected in the railway scenario. Finally, the specification of the output data - the track area classification results - is also analysed and provided. The specification of the survey measurement system and software toolset addressed in the current document will support the next project activities related to the development of the software tools for the classification of track areas in WP4.2 and the execution of measurement campaigns in WP4.3.

This document is the deliverable D4.3-Prototype Implementation of the Survey Toolset, which provides a high level description of the Toolset Prototype. This document is released in a pre-final version, where the toolset implementation has been done using demo data coming from RFI-ASTS private campaign in Italy. The dataset, related to the Pinerolo-Torino line, was used to test and debug the toolset, and the results provided in this document refer to that line and campaign. The Consortium thanks RFI, ASTS and TRI for the investment done to this aim and the availability of needed dataset in order to respect the scheduling of the project. The toolset will be definitively designed after the test campaigns on field, which will confirm the assumptions and in particular the decision logic approach. In fact, once the test campaigns foreseen in the ERSAT GGC will be performed - as planned in the WP4.3 task - the final implementation of the toolset prototype will be released based on those data. A conclusive issue of this deliverable will be then provided with the results of the test campaigns.

This document reports the execution of the track area classification process developed during the work package « WP4 Track Survey and Track Classification » along with the main outcomes and results obtained during this process. The goal of WP4 is to set up a standard measurement and classification system that is able to provide a prediction of the expected presence of GNSS hazard causes and degraded performance to be expected at a given track area, in order to enable the evaluation if the specific environment is suitable or not to place virtual balises or on the contrary should be discarded beforehand. In this document, we detail the results of applying the classification standard process, by means of the developed software Toolset to the measurement data collected in the three measurements campaigns during ERSAT GGC project. This execution report addressed the main important aspects that need to be carried out for the correct application of the classification process and the results obtained with the data collected.

ITST 2018, 16th International Conference on Intelligent Transport Systems Telecommunications, Lisbonne, PORTUGAL, 15-/10/2018 - 17/10/2018; The introduction of the GNSS positioning technology into the evolution of ERTMS/ETCS is based on the concept of virtual balise to minimize the impact onto existing ERTMS solutions. The detection of virtual balises is foreseen by using GNSS in combination with odometry information. However, the presence of local GNSS threats (e.g, multipath, NLOS or interference) may lead to unbounded errors in the pseudorange measurement that cannot be corrected by local or wide-area augmentation systems, and will lead ultimately to an error in the virtual balise position that cannot be bounded with the required integrity. In order to a priori prevent the risk of this hazardous situation, virtual balises must be logically located in areas where there are not local threats that may lead a Virtual Balise Transmission System on board the train to dynamically estimate unbounded virtual balise position errors. This paper summarizes the initial work performed in the H2020 GSA Project ERSAT-GGC with respect to the different techniques that can be used to detect local GNSS threats and that can support a later classification of railway areas as suitable or not suitable for placing virtual balises.

The deliverable D4.1 Procedure Specification Document of the ERSAT-GGC project is related to the work performed in Task 4.1. The aim of WP4 is to set up a standard measured system that is able to provide a forecast method for the GNSS service performance to be expected at a given track point, in order to enable evaluation whether the specific scenario is favourable to place virtual balises or on the contrary should be discarded beforehand. The results of this assessment can be included in a database map where geo-referenced GNSS quality data is stored. Users may access the information via specified interfaces using standardized communication channels. In order to set up the system, at first a standardized process has to be defined and specified that will determine what has to be measured under which conditions and what has to be simulated for GNSS characterizing along the line. This is the scope of Task 4.1. In order to set the context of the work, a description of the need and importance of a standard process to classify the track areas for virtual balise placement is given in the introduction. Later, a first version of the high level requirements to the classification process is provided. Based on these requirements a preliminary study of the different techniques, measurements and parameters that are relevant to define a safe track area classification standard process has been performed. The document then focus on a detail description on the GNSS detection techniques that are relevant for the standard process and the track area classification criteria. Further works related to the detection and classification criteria are required, and they will be included in the next deliverable D4.2, which specifies the dedicated requirements for the toolset.The standard process will undergo reviews until the execution and test: all these improvements and the related links to the toolset development will be covered by the next deliverables of WP4.

The adoption of the GNSS for the European Rail Train Management System – ERTMS is one of the objectives included into the ERTMS evolution plan to improve its competitiveness on the market. To this aim, in 2015 the European Global Navigation Satellite System Agency (GSA) approved the “ERTMS on SATELLITE –Enabling Application Validation (ERSAT–EAV)”, project with the object to verify the suitability of GNSS to generate Virtual Balises as an alternative/complement means to the physical balises prescribed by the ERTMS. The architecture of ERSAT EAV – based on joint use of multiple constellations and public augmentation networks - would be a contribution to foster cost-effective ERTMS solutions, primarily for the local and regional lines in Europe for which the economical sustainability is a priority. The paper describes the hazard’s mitigation techniques affecting the on board GNSS receiver when operating in harsh environments, and the scenarios for a smooth introduction of GNSS into the ERTMS system. Field test results carried out on a Test Bed implemented on a 50km line in Sardinia are presented and discussed.

The evolution plan of the European Railways Train Management System (ERTMS) includes the GNSS localization as one of the Game Changer technologies to improve the competitiveness of the ERTMS. GNSS will allow the implementation of cost-effective solutions to reduce the maintenance and operational cost without reducing the safety levels required by railway operations. The inherent low power of satellite navigation signals exposes GNSS-based solution to Radio Frequency threats, namely intentional or unintentional interference, that can lead to performance degradation or denial of service, and to spoofing/meaconing attacks, that can lead to receiver deception and hence to misleading PVT (Position, Velocity and Time) estimation. The aim of this paper is to present an architecture for detection and mitigation of radio-frequency hazards in a rail operational environment. The investigated solution is based on a Digital Beamforming Platform (DBP) coupled with advanced GNSS signal processing techniques for high rejection of GNSS interfering and counterfeit signals. This approach fully exploits the characteristics of the railway context, to support the evolution of the Location Determination System (LDS) based on GNSS in ERTMS Train Control System (TCS). This paper presents the DBP architectural design, focusing on the most meaningful and innovative solution foreseen for the prototype implementation. Each subsystem of the DBP is described in details, and a preliminary assessment of the performances is provided, by means of simulative and analytic tools.

In the frame of GATE4Rail project, Work Package (WP) 3 deals with the definition of a comprehensive methodology and the associated tools able to characterise the GNSS performance into the railways application scenarios defined in WP2 in the framework of a Virtual Balise detection in ETCS/ERTMS context. The present document (deliverable D3.1) is the first report delivered by GATE4Rail WP3. It is specifically dedicated to the definition of a standardised database in order to characterize GNSS in different rail scenarios and the description of Consortium tools included in the GATE4Rail test-bed able to generate local and global hazards and evaluate their impact on GNSS performance

WCRR 2022, World Congress on Railway Research, Birmingham, ROYAUME-UNI, 06-/06/2022 - 10/06/2022; Novel signalling systems are expected to use Digital Maps to implement Automatic Train Operation (ATO) functions or to augment technologies such as European Global Navigation Satellite Systems (EGNSS), inertial sensors or optical sensors for localization purposes. However, the construction of a Digital Map requires with current technologies expensive railway surveying campaigns. Additionally, the cost for their maintenance and update is a major challenge that prevents its adoption. With regard to the evolution of Control-Command and Signalling (CCS) systems, a new verification infrastructure is also needed to characterize new on-board solutions in terms of train position, speed and acceleration errors, as well as to assess their expected performance. Our research, on one side, aims to develop a methodology and related toolset for executing railway surveying, building and maintaining Digital Maps based on the post-processing of data recorded by trains in commercial service, equipped with Commercial Off The Shelf (COTS) sensors like GNSS receivers, cameras, Inertial Measurement Units (IMUs), and LIDARs. On the other side, a similar approach and development can be used to provide « high precision and high accuracy ground truth reference data » as true values for carrying out error and performance analysis. This Ground Truth methodology does not require installation of trackside equipment or modifications to existing trackside signalling systems and a-priori knowledge of track databases. This paper summarizes the main user needs of (1) the use of Digital Maps for signalling subsystems purposes, (2) the availability of innovative Ground Truth sources for performance assessments. These user needs are essential for the future design of such methodologies and toolsets.