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Edge-AI building blocks for CCAM systems

This call aims to develop approaches, verification, and training for Edge-AI building blocks in Connected, Cooperative, and Automated Mobility (CCAM) systems. Projects should focus on enhancing CCAM solutions with reduced power consumption, latency, and improved speed and accuracy by adapting sector-agnostic advancements in AI and cloud-edge-IoT technologies. The goal is to incorporate Edge-AI solutions into the perception, decision-making, and actuation chain of CCAM functionalities, ensuring safety, cybersecurity, privacy, and ethical standards. Projects should optimize AI algorithms for edge deployment, develop tools for model monitoring, and demonstrate applicability using real-world CCAM scenarios.

Opening Date: 16 September 2025
Deadline: 13 January 2026

Data provided by Sciencebusiness.net

This funding opportunity represents a pre-agreed draft that has not yet been officially approved by the European Commission. The final, approved version is expected to be published in the first quarter of 2025. This draft is provided for informational purposes and may be used to preliminarily form consortia and develop project ideas, but it is offered without any guarantees or warranties.

Expected Outcome

Projects are expected to deliver CCAM solutions with reduced power consumption and improved performance using Edge-AI, enhance safety and security standards, develop well-balanced AI computation distributions, validate approaches incorporating Edge-AI into CCAM systems, and provide tools for training and verification of edge-optimized AI models.

Scope

The call focuses on developing optimized Edge-AI algorithms and approaches for CCAM solutions that can run on edge-specific hardware with limited computational resources. Projects should adjust AI models for edge deployment, ensuring real-time insights and responses with reduced costs. The integration of AI with edge computing aims to enhance the full action chain from sensing to actuation in CCAM functionalities, addressing challenges like power consumption, speed, accuracy, scalability, and cost.

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