To successfully overcome these challenges, proposed actions are expected to address all of the following aspects:

• For next major advancements in AI applications in CCAM solutions, huge AI applications need to fit into limited hardware, to make it fit for purpose. Edge-AI devices often have limited computational resources, making it challenging to deploy large and complex AI models. Thus, it is essential to develop and reshape approaches and building blocks for CCAM solutions, viable to be run on edge-hardware. Use cases for the approaches and building blocks should focus on time-critical applications (such as the chain from (collective) perception, decision making and actuation of functionalities) and can be linked to the activities and results from projects AI4CCAM and AIthena.
• Develop optimised edge-AI algorithms and demonstrate their applicability and scalability, using real-world CCAM scenarios such as in the databases resulting from projects such as SYNERGIES[3]. The development and demonstration use case should include in-vehicle perception and understanding, such as object detection, segmentation, road surface tracking, sign and signal recognition, etc. Decision making and actuation of countermeasures is to be part of the chain of actions. The approaches for these building blocks and enabling technologies should facilitate a quick uptake in adjacent or following projects;
• Optimisation of the models for edge deployment. This involves adjusting the size and complexity of models to allow it to run on the relevant edge devices and include training and verification approaches. Techniques such as model quantization, pruning, and knowledge distillation can be used to reduce the size of AI models without significant loss in performance. Additionally, over-the-air (OTA) updates can be used to manage and update models across a fleet of devices efficiently;
• Develop tools and approaches for edge-AI model monitoring, to ensure that edge-AI systems continue to operate as expected and ensure resilience to failure conditions or attacks, and monitoring model outputs to ensure they are accurate even as real-life conditions and datasets change.

The research will require due consideration of cyber security, connectivity and both personal and non-personal data protection rules, including compliance with the GDPR, and ensure that gender and other social categories (such as but not limited to disability, age, socioeconomic status, ethnic or racial origin, sexual orientation, etc.), and their intersections are duly considered where appropriate, as well as Explainable AI to enhance trust and regulatory compliance including alignment with the AI Act.

In order to achieve the expected outcomes, international cooperation is encouraged in particular with Japan and the United States but also with other relevant strategic partners in third countries. Such cooperation should exploit synergies in edge AI approaches for mobility and for CCAM, as well as its integration into the vehicle architecture.