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High integrity EGNSS Layer for Multimodal Eco-friendly Transportation

Periodic Reporting for period 1 - HELMET (High integrity EGNSS Layer for Multimodal Eco-friendly Transportation)

Reporting period: 2020-01-02 to 2021-01-01

Main objectives of HELMET are:
1.To develop a cyber-secured multimodal, multi-sensor integrity monitoring architecture based on EGNSS to introduce High Integrity Location Determination System (LDS) for cars and trains automation aggregating the demand of monitoring rail and road assets with UAV, with the following properties:
o implementation of multi-frequency and multiple constellations solutions (i.e. at least GPS and Galileo) Signal In Space (SIS) integrity monitoring, including code pseudoranges, carrier phase and Doppler, together with EGNOS as well as its future evolution (to V3);
o exploitation of the high accuracy services and the authentication features provided by Galileo;
o full support of high on-board safe positioning and monitoring by sensor fusion and combination;
o standard authenticated interface for providing Authenticated High Integrity and High Accuracy services for multiple applications (i.e. rail, automotive, UAV for rail and road applications);
o full compliance with European requirements and regulations
2.To assess the system performance by a Proof-of-Concept (PoC), creating a liaison with EMERGE, the Italian project based on Galileo and 5G for the connected car, to exploit the urban test bed in the town of L’Aquila. The PoC will be realised by exploiting both real data collected in the field and synthetic data used to reproduce rare GNSS SIS (Signal-In- Space)
fault events, including satellite malfunctions, anomalous atmospheric behaviours and local faults affecting individual users, such as multipath, shadowing, intentional (i.e. jamming, spoofing and meaconing) and unintentional interferences occurring in the selected environment. Through this activity, the aim is to verify:
> the feasibility of the multi-modal AIMN primarily for railways and connected cars, previously described;
> the ability of this Augmentation Network to enable services provided by UAVs in support of rail and road services;
> the capability to detect and mitigate local effects on the OBU in order to provide integrity monitoring to the user.
3.To draw a roadmap for exploitation and future standardization and certification of HELMET results in terms of (a) the designed multi-modal AIMN architecture, and (b) high integrity and accuracy OBU algorithms fully customized for land transportation (rail and road). This will be carried out by exploiting the synergy between road and rail and taking into account the outcomes of the previous H2020 GSA projects, including RHINOS, ERSAT-EAV, STARS and ERSAT-GGC, where RDL and other partners have been involved. A strong relationship with the Italian project EMERGE, coordinated by RDL with FCA (Fiat Chrysler Automobiles), will be performed for derivation and harmonization of system requirements for connected and semiautonomous car applications. Moreover, the EMERGE test-bed will be used for HELMET proof-of-concept set-up and validation. Finally, a steering committee, involving different rail, road and UAV stakeholders, and associations (as the RTCM Committee, “Working Group-C on Next Generation GNSS”) will be created, since the project beginning, for the derivation of user needs, specific use cases and harmonization of functional, performance and RAMS (Reliability, Availability, Maintainability, and Safety) system specifications.
All activities have been mainly focused on Objective 1 with its achievement of 70% of the total, being this foreseen to be reached during the 2nd RP of the project, at the completion of the WP4 activities. In particular, the WP2 and WP3 activities were completed and relative Deliverables released and approved. Subsequently, activities on WP4 were started and currently are on-going. The 1st phase of HELMET has been devoted to defining the user requirements for road and rail and UAV (supporting the first two ones) applications and system requirements (including multimodal AIMN and multi-sensor OBU platform). This activity has been carried out in the WP2 and completed. The 2nd phase, the design phase, has been objective of WP3 and foresaw two sub-phases: preliminary design and detailed design. The 1st sub-phase has been linked to the functional architecture and the subsystem definition while the 2nd one has been devoted to the architecture tailoring. This activity has been carried out in this WP and completed. The 3rd phase is represented by the system development and testing and is object of WP4. This phase is on-going and includes the development of each subsystem architecture together with the unit and integration testing of the final HELMET architecture. Achieved results have been the following: 1) preliminary definition of the development report and test procedures for the Rail and Automotive MOBU and identification of the testing activities; 2) implementation of the core functionality of the Record and Playback unit on the purchased HW and definition and coordination of the field data collection campaign; 3) definition of the AIMN development blocks, the development of functional blocks and tests. Other important activity currently on-going is the one relative to the definition of a Roadmap for Exploitation, Standardization and Certification aspects fundamental for the development of a large-scale application as the rail and automotive ones. The WP6 has been devoted to address such activity. Achieved results have been the following 1) definition of the standardization and certification environment; 2) definition of a high-level roadmap for the standardisation; 3) start dissemination activity into the RTCM-134 Standardization Committee. Finally, the activities related WP7, have been mainly focused on dissemination and communication activities.
Multi-modality is a key differentiator of HELMET to aggregate the needs of cars and trains with a consequential effect to catch the demand of UAVs for monitoring rail and road assets.In this way HELMET contributes to foster the EGNSS market uptake in transport. Moreover, it aims at improving the transportation means efficiency through eco-friendly solutions for the benefit of all citizens, economy and society.The high accuracy localization platform of HELMET will reduce the safe-distance between two vehicles contributing to increase the capacity of transport especially on peak-hours avoiding constructing new roads and railways.HELMET creating a liaison between train and cars ecosystems, will favour the exploitation of the synergies between these sectors through the sharing of the expertise on the safety and certification process acquired in the train domain, and the opportunity of new multi-sensors platforms that the automotive market requires on a much larger scale.Furthermore HELMET will contribute to the definition of a roadmap to fulfil the standardisation and certification process, considering the regulatory requirements and user needs, and operational requirements coming from many different land transportation operators and actors involved.Finally HELMET will exploit a strategy based on the creation of a service provider targeting the rail and road segments and involving private and institutional actors.
Multi-modal AIMN Architecture scheme
HELMET Multimodal AIMN and MOBU cocept
Multi-sensor on-board unit concept at glance