HORIZON EUROPE
Europeo
Specific Challenge:Increasing the Automation in ATM is considered as a key to significantly improve ATM performance. However, ATM is a continuous 24-7 set of services where the complexity of the ATM system, its fallback modes and necessary recovery steps has proved to be a major challenge for the introduction of further automation, and this has consequently slowed down the advancement of automation in ATM, especially in the most congested areas of Europe. The latest progress in the domain of Artificial intelligence and in particular Machine Learning may open new possibilities for further automation in ATM in high-density operations and some new applications have already been developed in SESAR.The application of Machine Learning for Automation in ATM also comes with new challenges, including sound assurance arguments that need to be solved to avoid a negative impact. In particular safety, business continuity and cyber security issues need to be proposed at an early stage of development of the automation concept.
One challenge of increasing automation is related to transparency of the automated system. Any automated assistance system needs to be able to provide the huma...
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Specific Challenge:Increasing the Automation in ATM is considered as a key to significantly improve ATM performance. However, ATM is a continuous 24-7 set of services where the complexity of the ATM system, its fallback modes and necessary recovery steps has proved to be a major challenge for the introduction of further automation, and this has consequently slowed down the advancement of automation in ATM, especially in the most congested areas of Europe. The latest progress in the domain of Artificial intelligence and in particular Machine Learning may open new possibilities for further automation in ATM in high-density operations and some new applications have already been developed in SESAR.The application of Machine Learning for Automation in ATM also comes with new challenges, including sound assurance arguments that need to be solved to avoid a negative impact. In particular safety, business continuity and cyber security issues need to be proposed at an early stage of development of the automation concept.
One challenge of increasing automation is related to transparency of the automated system. Any automated assistance system needs to be able to provide the human operator with all information necessary to enable an understanding of the reasons for its behaviour and/or decisions. Otherwise the system may not be accepted or trusted by the operator, thus negating the theoretical benefits of the automation.
Another challenge is the Generalization of results from Machine Learning methods. Differences between the data used for training and the data feed to a trained algorithm can lead to unexpected results, including that not all situations can be anticipated during the training. Additionally, due to this behaviour, the system might not be able to adapt to changes of behaviour of other actors that couldnot be anticipated during the training of the system. In order to certify systems based on Machine- Learning, methods are needed to demonstrate that in delegating the control to these system sufficient assurance can be provided that it does not raise safety risks beyond what can be mitigated by other measures. Moreover, concerns have been raised that this behaviour may add uncertainty or unexpected behaviour to the ATM system.
Scope:Proposals should select a specific ATM operational environment, present a vision of a higher level of automation in this operational environment (which may include the delegation of control to the automation) and address one or more of the specific challenges above that hinder the application of machine learning methods for the further automation of ATM (e.g. Transparency, Generalization). Proposals should aim at providing a better understanding of this challenge(s) and investigate innovative methods to address the(se) challenge(s) in ATM. Proposals may make assumptions about the availability of technology and/or operations enablers (e.g. data link), but need to state them clearly. This topic covers ground and airborne automation that impacts ATM.
Projects addressing the Transparency of automated systems incorporating machine learning methods required for cooperative human machine systems should identify which information needs to be provided to enable the human operator to cooperatively work together with the automation. Based on the identified information requirements, the project should select or develop and assess suitable machine learning methods for ATM automation that are able to provide these kind of information and assurances. The project may investigate the applicability of methods from the domain of Explainable Artificial Intelligence (XAI).
Projects addressing the Generalisation and the adaptation of the algorithms to changes in the operational environment, should investigate methods to estimate and increase the ability of an automated systems to handle a situations that were not foreseen during the development and training. These methods should enable automation to adapt to changes of the environment, like the change of behaviour of some actors (e.g. modification of operational procedures), the entrance of new actors or unforeseen traffic or weather situations. Projects may explore the possibility to apply algorithms able to learn during operation in order to adapt to optimise operations based on changes in the environment. Projects may also investigate the effects of uncertainty added to operations by these new methods.
Research activities may take aspects related to certification into account as required. However, this shall not be the primary focus of research in the proposal as this will be addressed in topic SESAR-ER4- 09-2019.
Proposals can also suggest to address other challenges of applying AI machine learning for ATM Automation other than these mentioned above if justification is provided.
Expected Impact:Projects are expected to provide principles that could enable higher levels of automation that are predicted to lead to an improvement of ATM performance, in particular cost efficiency, capacity and safety.
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Características del consorcio
:
La ayuda es de ámbito europeo, puede aplicar a esta linea cualquier empresa que forme parte de la Comunidad Europea.
Características del Proyecto
Los costes de personal subvencionables cubren las horas de trabajo efectivo de las personas directamente dedicadas a la ejecución de la acción. Los propietarios de pequeñas y medianas empresas que no perciban salario y otras personas físicas que no perciban salario podrán imputar los costes de personal sobre la base de una escala de costes unitarios
Los otros costes directos se dividen en los siguientes apartados: Viajes, amortizaciones, equipamiento y otros bienes y servicios. Se financia la amortización de equipos, permitiendo incluir la amortización de equipos adquiridos antes del proyecto si se registra durante su ejecución. En el apartado de otros bienes y servicios se incluyen los diferentes bienes y servicios comprados por los beneficiarios a proveedores externos para poder llevar a cabo sus tareas
La subcontratación en ayudas europeas no debe tratarse del core de actividades de I+D del proyecto. El contratista debe ser seleccionado por el beneficiario de acuerdo con el principio de mejor relación calidad-precio bajo las condiciones de transparencia e igualdad (en ningún caso consistirá en solicitar menos de 3 ofertas). En el caso de entidades públicas, para la subcontratación se deberán de seguir las leyes que rijan en el país al que pertenezca el contratante
Características de la financiación
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon 2020 projects. See the information in the Online Manual.
2. Eligibility and admissibility conditions: described in Annex B and Annex C of the H2020 Work Programme and in the SJU Single Programming Document 2019-2021.
Proposal page limits and layout:
The title, list of participants and sections 1, 2 and 3, together, should not be longer than 35 pages, as established in the Call conditions for this call.
Please note that this page limit is lower than the standard one for Research and Innovation Actions.
Please refer to Part B of the SJU proposal template in the electronic submission system.
3. Evaluation:
Evaluation criteria, scoring and thresholds are described in the SJU Single Programming Document 2019-2021.
Submission and evaluation processes are described in the Online Manual.
4. Indicative time for evaluation and grant agreement:
... 1. List of countries and applicable rules for funding: described in Annex A of the H2020 Work Programme and in the SJU Single Programming Document 2019-2021.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon 2020 projects. See the information in the Online Manual.
2. Eligibility and admissibility conditions: described in Annex B and Annex C of the H2020 Work Programme and in the SJU Single Programming Document 2019-2021.
Proposal page limits and layout:
The title, list of participants and sections 1, 2 and 3, together, should not be longer than 35 pages, as established in the Call conditions for this call.
Please note that this page limit is lower than the standard one for Research and Innovation Actions.
Please refer to Part B of the SJU proposal template in the electronic submission system.
3. Evaluation:
Evaluation criteria, scoring and thresholds are described in the SJU Single Programming Document 2019-2021.
Submission and evaluation processes are described in the Online Manual.
4. Indicative time for evaluation and grant agreement:
Information on the outcome of single-stage evaluation: maximum 5 months from the deadline for submission.
Signature of grant agreements: maximum 8 months from the deadline for submission.
5. Proposal templates, evaluation forms and model grant agreements (MGA):
SESAR JU Research and Innovation Action (SESAR-RIA)
Specific rules and funding rates
Proposal templates are available after entering the submission tool below.
ER4 Proposal templates
SESAR JU MGA - Multi-Beneficiary
H2020 Annotated Grant Agreement
6. Additional requirements:
The SESAR JU considers that proposals addressing topics in Work Area 1 can request a contribution from the EU between EUR 500.000 minimum and EUR 1.000.000 maximum and should end no later than Q4 2022 (including 6 months for dissemination activities after delivering final results). These conditions are intended to allow the specific challenges to be addressed appropriately and if additional EU contribution is requested this must be strongly justified in any proposal.
Horizon 2020 budget flexibility
Classified information
Financial support to Third Parties
Members of consortium are required to conclude a consortium agreement, in principle prior to the signature of the grant agreement.
7. Open access must be granted to all scientific publications resulting from Horizon 2020 actions.
Where relevant, proposals should also provide information on how the participants will manage the research data generated and/or collected during the project, such as details on what types of data the project will generate, whether and how this data will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.
Open access to research data
The Open Research Data Pilot has been extended to cover all Horizon 2020 topics for which the submission is opened on 26 July 2016 or later. Projects funded under this topic will therefore by default provide open access to the research data they generate, except if they decide to opt-out under the conditions described in Annex L of the H2020 main Work Programme. Projects can opt-out at any stage, that is both before and after the grant signature.
Note that the evaluation phase proposals will not be evaluated more favourably because they plan to open or share their data, and will not be penalised for opting out.
Open research data sharing applies to the data needed to validate the results presented in scientific publications. Additionally, projects can choose to make other data available open access and need to describe their approach in a Data Management Plan.
Projects need to create a Data Management Plan (DMP), except if they opt-out of making their research data open access. A first version of the DMP must be provided as an early deliverable within six months of the project and should be updated during the project as appropriate. The Commission already provides guidance documents, including a template for DMPs. See the Online Manual.
Eligibility of costs: costs related to data management and data sharing are eligible for reimbursement during the project duration.
The legal requirements for projects participating in this pilot are in the article 29.3 of the Model Grant Agreement.
8. Additional documents
Please read carefully all documents below before the preparation of your application. The key reference documents below set out the purpose and scope of the Exploratory Research 4 Call for Proposals and describe the activities that will be implemented via the resulting Grant Agreements as well as the conditions for participation and award.
SJU Single Programming Document 2019-2021
Project Handbook of SESAR 2020 Exploratory Research Call ER4 - Programme Execution Guidance
Technical Specification of SESAR 2020 Exploratory Research 4 Call - ER4
ATM Master Plan
Communication Guidelines S2020 Projects
Frequently Asked Questions & Answers (all updates are constantly reflected in the document)
H2020 Regulation of Establishment
H2020 Rules for Participation
H2020 Specific Programme
Información adicional de la convocatoria
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