Descripción del proyecto
La carrera de la inteligencia artificial por una movilidad conectada y automatizada
Nunca ha habido tantos datos, y la inteligencia artificial (IA) nunca ha tenido un potencial tan grande para entrenar a los vehículos autónomos. En este contexto, han surgido tecnologías de movilidad automovilística conectadas y cooperativas, pero la IA sigue sin estudiarse en términos de explicabilidad, preservación de la privacidad, ética y responsabilidad. En el proyecto Althena, financiado con fondos europeos, se sentarán las bases de una IA fiable, que se utilizará en toda su capacidad en beneficio de la sociedad. El equipo contribuirá a crear IA explicable (IAE), investigando datos, modelos y pruebas, desarrollará una metodología centrada en el ser humano y propondrá un conjunto de indicadores clave del rendimiento sobre IAE. Además, los datos y las herramientas estarán disponibles a través de iniciativas europeas de intercambio de datos.
Objetivo
Connected and Cooperative Automotive Mobility (CCAM) solutions have emerged thanks to novel Artificial Intelligence (AI) which can be trained with huge amounts of data to produce driving functions with better-than-human performance under certain conditions. The race on AI keeps on building HW/SW frameworks to manage and process even larger real and synthetic datasets to train increasingly accurate AI models.
However, AI remains largely unexplored with respect to explainability (interpretability of model functioning), privacy preservation (exposure of sensitive data), ethics (bias and wanted/unwanted behaviour), and accountability (responsibilities of AI outputs). These features will establish the basis of trustworthy AI, as a novel paradigm to fully understand and trust AI in operation, while using it at its full capabilities for the benefit of society.
AITHENA will contribute to build Explainable AI (XAI) in CCAM development and testing frameworks, researching three main AI pillars: data (real/synthetic data management), models (data fusion, hybrid AI approaches), and testing (physical/virtual XiL set-ups with scalable MLOps).
A human-centric methodology will be created to derive trustworthy AI dimensions from user identified group needs in CCAM applications. AITHENA will innovate proposing a set of Key Performance Indicators (KPI) on XAI, and an analysis to explore trade-offs between these dimensions.
Demonstrators will show the AITHENA methodology in four critical use cases: perception (what does the AI perceive, and why), situational awareness (what is the AI understanding about the current driving environment, including the driver state), decision (why a certain decision is taken), and traffic management (how transport-level applications interoperate with AI-enabled systems operating at vehicle-level).
Created data and tools will be made available via European data sharing initiatives (OpenData and OpenTools) to foster research on trustworthy AI for CCAM.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
20009 Donostia San Sebastian
España
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.