Deliverables

The purpose of the Quality and Risk Plan is to provide detailed process requirements with respect to the day-to-day project management and on the particular practical aspects of the project development, including report production procedures, criteria for work progression and results performance measurement. This deliverable shall be used by all the project partners in order to ensure quality assurance of project processes and outputs and prevent possible deviations from the project work plan.

A DMP describes the data management life cycle for the data to be collected, processed and/or generated by a project.

This deliverable provides a description of the meaning of the Ground Truth and the Digital Map concepts and of the user needs related to them. Such user needs have been contextualized in the context of the ERTMS train position and odometry principles and the interoperability performance requirements specified in Subset 041. Some practical use cases that can get benefits from the Ground Truth and the Digital Map have been also described with qualitative and quantitative examples.

The contents of this document includes the design of the High Level Logical Functional Architecture of the RAILGAP infrastructure capable of meeting the user requirements identified during WP2.1 activities and reported in RAILGAP D2.1.

Specification of the Measurement Information” provides the description of the measurement information to be acquired from each sensor and technology selected to be part of the RAILGAP Infrastructure and of the procedures to be adopted for executing the measurements. It also provides indications on the types of measurement equipment selected.

Specification and Description of the Measurement Infrastructure” provides a general overview of the RAILGAP measurement environment and a detailed description of the measurement configuration settings and measurement procedures to collect the measurement information introduced into the deliverable D3.1.

This deliverable provides the Electrical and Mechanical Design of the RAILGAP measurement infrastructure. Starting with the analysis of the main relevant standards applicable in the european railway context, the deliverable reports the electrical and mechanical drawings that have been realized to support the installation of the RAILGAP on-board measurement subsystem on the three different measurement trains (i.e. two in Italy and one in Spain)

The document contains first introduction to each of the sensor technologies that are considered in RAILGAP and the key important elements for the use of the sensors for different use cases related to localization systems and for the main goals of RAILGAP on the elaboration of railway ground truth and digital map generations. This deliverable covers different aspects, error isolation techniques, modeling methodologies and initial results for some of the sensors in this first phase

Deliverable D4.4 summarizes the designs and relevant methods to perform the multi-sensor fusion. This deliverable is focused on the general fusion algorithms considering that sensor fault detection, discard algorithms and multi-sensor fault detection and exclusion techniques developed in other activities of the project are applied, providing a more robust and reliable sensor fusion solution.

This deliverable defines the architectural characteristics and the the main functionalities related to the functional blocks of the RAILGAP infrastructure: the On-Board Measurement (OBM) Unit and the Trackside Data Collector (TDC) Unit, also called Trackside Diagnostic Collector. Furthermore it defines the interfaces between OBM and TDC and between TDC and the data consumers (i.e., Technology Characterization, Digital Map, and Ground Truth).

This deliverable specifies the requirements of the On-Board Measurement (OBM) Unit and outlines the equipment implemented on the trial vehicles.

This deliverable specifies the requirements of the Trackside Data Collector (TDC) Unit, also called Trackside Diagnostic Collector. It also outlines Mission Log Files naming scheme and the file system structure used for their storage in the data repository.

The document contains the details of the Ground Truth concept definition, in terms of variables, Key performance Indicators, and Minimum expected performances. A preliminary approach to the architectural design is also provided, with the associated requirements for the Data Fusion Algorithm, Ground Truth Construction, Output Management and User Interface.

This deliverables aims at 1. Defining the functional and performance requirements of trackside digital map in terms of accuracy, associated confidence interval and errors. 2. Specifying trackside elements and conditions to be considered for the digital map compliance with the railway standardised format 3. Specifying sensor data/information to be used as input for each identified algorithm for digital map design and reconstruction. 4. Defining technical performance required by multi-sensor platform in order to consider train dynamic effects of vehicles on tracks 5. Performing a survey of state of art of RTK/PPP DFMC algorithms and used sensor technologies definition, swot analysis and identification of candidate algorithms able to exploit the collected data for building the digital map; 6. Studying the state of the art of the different geolocation technologies based on IMU, Lidar or Digital cameras, identify which can be effectively combined with RTK / PPP and study the algorithms that allow combining both information to improve positioning. These algorithms include techniques of AI and video analysis to identify objects that can be used as a reference to improve positioning locally.

This deliverable addresses the ethics issues which may raise within RAILGAP project, related to the Humans requirement. This deliverable describes data protection and ethical issues considered within the RAILGAP project.

This deliverable provides the information on the procedures that will be implemented for data collection, storage, protection, retention and destruction of field measurement data during the field campaigns.