Innovating Works

H2020

Cerrada
HORIZON-CL5-2022-D6-01-05
HORIZON-CL5-2022-D6-01-05: Artificial Intelligence (AI): Explainable and trustworthy concepts, techniques and models for CCAM (CCAM Partnership)
ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:
Sólo fondo perdido 0 €
Europeo
Esta convocatoria está cerrada Esta línea ya está cerrada por lo que no puedes aplicar. Cerró el pasado día 12-01-2022.
Se espera una próxima convocatoria para esta ayuda, aún no está clara la fecha exacta de inicio de convocatoria.
Por suerte, hemos conseguido la lista de proyectos financiados!
Presentación: Consorcio Consorcio: Esta ayuda está diseñada para aplicar a ella en formato consorcio..
Esta ayuda financia Proyectos:

ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:

Concepts, techniques and models based on Artificial Intelligence (AI) used for situational awareness, prediction, decision making and triggering of actions for time critical and safety relevant CCAM applications as well as for cyber threat detection and mitigation.A clear understanding of the capabilities, limitations and potential conflicts of AI based systems for CCAM.Increased user acceptance from an early stage, based on explainable, trustworthy and human-centric AI. Interactions with vehicles using AI should be understandable, human-like and reflect human psychological capabilities, and free of gender, ethnic or other biases.Accelerated AI development and training for CCAM enabled by a relevant set of real and synthetic traffic events and scenarios.AI based CCAM solutions will evolve from reactive and/or adaptive system support into predictive system state awareness (including driver state and user diversity), decision-making and actuation, enhancing road safety especially in near-critical situations.
Scope:The deterministic understanding and consequential... ver más

ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:

Concepts, techniques and models based on Artificial Intelligence (AI) used for situational awareness, prediction, decision making and triggering of actions for time critical and safety relevant CCAM applications as well as for cyber threat detection and mitigation.A clear understanding of the capabilities, limitations and potential conflicts of AI based systems for CCAM.Increased user acceptance from an early stage, based on explainable, trustworthy and human-centric AI. Interactions with vehicles using AI should be understandable, human-like and reflect human psychological capabilities, and free of gender, ethnic or other biases.Accelerated AI development and training for CCAM enabled by a relevant set of real and synthetic traffic events and scenarios.AI based CCAM solutions will evolve from reactive and/or adaptive system support into predictive system state awareness (including driver state and user diversity), decision-making and actuation, enhancing road safety especially in near-critical situations.
Scope:The deterministic understanding and consequential design of assistance systems are mostly reactive or to some extent adaptive. In the transition from driver assistance systems towards fully automated systems, a critical aspect is the decision making (i.e. planning and acting), based on robust and reliable detection and perception. AI has a huge potential to advance this process.

Specifically, in more complex and dense traffic environments, highly automated driving functions will benefit from the system state prediction enabled by AI. Yet, the current state of technology using AI for CCAM has limitations regarding human-like actions, more specifically the intuitive, split-second (predictive) assessments and ‘reflex decision making’. As such, any AI requires good integration into the overall system with close interaction and compatibility with the active safety systems (e.g. automated emergency braking).

For the development process, training is essential for the performance of unbiased AI. It requires sufficient traffic and event data under varying conditions from all over Europe, avoiding limited data sets. The current, mainly deterministic approaches for validation in automotive development will not be sufficient for future training and validation of AI-based or AI-supported functions, which will also need to be able to deal with complex issues as (un)intended miscommunication.

Proposed R&I actions therefore are expected to address all the following aspects

Support the development and integration of AI in CCAM with explainable, trustworthy and human-centric and unbiased concepts, techniques and models; this can be on vehicle level and on transport system level, where tactical and strategic links to traffic management and traffic conditions need to be established.Address the knowledge gap on AI training and validation approaches as well as efficient and ethical approaches for data handling of increasing amounts of data.Build upon existing and generated data for training and verification of AI supporting situational awareness in CCAM in more complex traffic scenarios (e.g. digital twins). Specific automotive requirements on functional safety and security need to be considered in the development process of an automotive-grade AI ensuring consistency with existing validation procedures.

This topic requires the effective contribution of SSH disciplines and the involvement of SSH experts, institutions, as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities.

In order to achieve the expected outcomes, international cooperation is advised, in particular with projects or partners from the US, Japan, Canada, South Korea, Singapore, Australia.

This topic implements the co-programmed European Partnership on ‘Connected, Cooperative and Automated Mobility’ (CCAM).


Specific Topic Conditions:Activities are expected to achieve TRL 5 by the end of the project – see General Annex B.




Cross-cutting Priorities:Digital AgendaCo-programmed European PartnershipsInternational CooperationSocial sciences and humanitiesArtificial Intelligence


ver menos

Temáticas Obligatorias del proyecto: Temática principal:

Características del consorcio

Ámbito Europeo : La ayuda es de ámbito europeo, puede aplicar a esta linea cualquier empresa que forme parte de la Comunidad Europea.
Tipo y tamaño de organizaciones: El diseño de consorcio necesario para la tramitación de esta ayuda necesita de:

Características del Proyecto

Requisitos de diseño: Duración:
Requisitos técnicos: ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes: ExpectedOutcome:Project results are expected to contribute to all of the following expected outcomes:
¿Quieres ejemplos? Puedes consultar aquí los últimos proyectos conocidos financiados por esta línea, sus tecnologías, sus presupuestos y sus compañías.
Capítulos financiables: Los capítulos de gastos financiables para esta línea son:
Personnel costs.
Subcontracting costs.
Purchase costs.
Other cost categories.
Indirect costs.
Madurez tecnológica: La tramitación de esta ayuda requiere de un nivel tecnológico mínimo en el proyecto de TRL 4:. Es el primer paso para determinar si los componentes individuales funcionarán juntos como un sistema en un entorno de laboratorio. Es un sistema de baja fidelidad para demostrar la funcionalidad básica y se definen las predicciones de rendimiento asociadas en relación con el entorno operativo final. + info.
TRL esperado:

Características de la financiación

Intensidad de la ayuda: Sólo fondo perdido + info
Fondo perdido:
0% 25% 50% 75% 100%
Para el presupuesto subvencionable la intensidad de la ayuda en formato fondo perdido podrá alcanzar como minimo un 100%.
The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations. The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations.
Garantías:
No exige Garantías
No existen condiciones financieras para el beneficiario.

Información adicional de la convocatoria

Efecto incentivador: Esta ayuda no tiene efecto incentivador. + info.
Respuesta Organismo: Se calcula que aproximadamente, la respuesta del organismo una vez tramitada la ayuda es de:
Meses de respuesta:
Muy Competitiva:
No Competitiva Competitiva Muy Competitiva
No conocemos el presupuesto total de la línea pero en los últimos 6 meses la línea ha concecido
Total concedido en los últimos 6 meses.
Minimis: Esta línea de financiación NO considera una “ayuda de minimis”. Puedes consultar la normativa aquí.

Otras ventajas

Sello PYME: Tramitar esta ayuda con éxito permite conseguir el sello de calidad de “sello pyme innovadora”. Que permite ciertas ventajas fiscales.