CFPs: Approaches, Verification and Training for Edge-AI Building Blocks for CCAM Systems

Project results are expected to contribute to all of the following expected outcomes: CCAM solutions – in hardware and software – with reduced power consumption, latency, and improved speed and accuracy, as domain specific adaptions of sector agnostic advancements in e.g. AI and/or cloud-edge-IoT technologies; Enhanced levels of safety, (cyber) security, privacy and ethical standards of data-driven CCAM functionalities by using e.g. edge-AI applications for CCAM; Approaches for well-balanced distributions of AI calculations for expanding use cases (e.g. collective perception, decision making and actuation) for connected, cooperative and automated driving applications (using a balanced mix of edge-based solutions, cloud-enabled solutions and vehicle-central solutions), balancing speed and latency, energy use, costs, data sharing and storage needs and availability; Validated approaches incorporating edge-AI solutions into the action chain from perception and decision-making up to actuation of advanced CCAM functionalities – both on-board and on the infrastructure side – for systemic applications such as traffic management and remote control, as well as tools and approaches for training of such functionalities, which require optimised and verified edge-AI models .

The scope of the call emphasizes that CCAM-enabled vehicles constantly sense their surroundings, requiring powerful and optimized large data processing algorithms. Current reliance on general-purpose hardware limits performance due to constraints like power consumption, speed, scalability, and cost. To address this, the initiative promotes a dual approach: complementing hardware advancements, such as those driven by the Chips JU calls, with efforts to optimize AI algorithms for CCAM functionalities.

Edge-AI, which processes data locally on hardware near the data source, enables real-time insights, reduces networking costs, and enhances resilience in safety-critical situations. Projects are expected to develop optimized edge-AI algorithms, validate them in real-world CCAM scenarios, and ensure their scalability. The focus includes applications like object detection, road surface tracking, and decision-making, all critical for automated driving systems.

The action aims to achieve Technology Readiness Level (TRL) 5 by the project’s conclusion, with eligible costs funded as lump sums. The indicative EU budget for this topic is EUR 4 million, with one project expected to be funded.

The Commission estimates that an EU contribution of around EUR 4.00 million would allow the outcomes to be addressed appropriately. Nonetheless, this does not preclude submission and selection of a proposal requesting different amounts.

The total indicative budget for the topic is EUR 4.00 million.

For more information, visit EC.