Expected Outcome:

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.

Scope:

CCAM-enabled vehicles are constantly sensing their surroundings on road conditions, location, nearby vehicles and infrastructure. Such data is shared in real-time, while data from other sources is received. This needs powerful and optimised large data processing algorithms, which requires large amounts of computing power, data processing, real-time operation and high levels of security. However, most existing AI computing tasks for automated vehicle applications are relying on general-purpose hardware, which has limitations in terms of power consumption, speed, accuracy, scalability, memory footprint, size and cost. Hardware advancements driven by initiatives such as the Chips JU calls must be complemented by significant efforts to optimise AI algorithms for CCAM functionalities, ensuring their efficient performance on edge-specific hardware.

To encompass CCAM solutions in future steps towards e.g., the Software Defined Vehicle, this dual approach on AI advancements and hardware advancements is essential. Complementarities with projects funded under Cluster 4 “Digital Industry and Space” of Horizon Europe should also be considered where appropriate, especially in translating sector-agnostic innovations to the specificities of CCAM applications. Requirements on AI algorithm optimisation, latency, on-board energy availability, solutions to gain unbiased datasets for AI training, Electronic Control Unit (ECU) capacity and on potential safety-critical scenarios should be considered to ensure the timely triggering of actions, and in a later stage, anticipatory driving. Solutions should use, as far as possible, building blocks, interfaces, and tools from projects of the Software-Defined Vehicle of the Future (SDVoF) initiative.

Edge-AI involves deploying AI algorithms on edge computing devices, which are hardware systems constrained in proximity to the data source where they operate. This is done without relying on remote resources for the computational efforts. It thus facilitates real-time insights, responses and triggering of actions, with reduced costs as the processing power close to the application is used, greatly reducing networking costs. Combining AI with edge-AI can facilitate stable solutions to include the full activity chain from sensing, perception, decision-making up to actuation of advanced CCAM solutions, gaining speed and resilience which are essential in safety-critical situations.

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[1] and AIthena[2].
• 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.

This topic implements the co-programmed European Partnership on ‘Connected, Cooperative and Automated Mobility’ (CCAM). As such, projects resulting from this topic will be expected to report on results to the European Partnership ‘Connected, Cooperative and Automated Mobility’ (CCAM) in support of the monitoring of its KPIs.

Projects resulting from this topic are expected to apply the European Common Evaluation Methodology (EU-CEM) for CCAM[4].

Projects funded under this topic are encouraged to explore potential complementarities with the activities of the European Commission's Joint Research Centre’s Sustainable, Smart, and Safe Mobility Unit and, where appropriate, establish formal collaboration.

AI Based Application Success Predictor

1️⃣ Strong, Mission-Aligned Impact (Most Important Across EC Calls)

The EC is impact-driven: proposals must show how the project will:

Solve a major European or global societal challenge

Deliver measurable, lasting benefits for EU citizens

Produce outputs that can be used by policymakers, industry, or society

Align with Horizon Europe missions, priorities, and strategic agendas

Predictor: Clear, quantifiable, EU-level impact → strongest scoring factor.

2️⃣ Clear, Ambitious, but Achievable Objectives

Successful proposals show:

2–4 well-defined objectives linked to the Work Programme call text

Clearly articulated research questions or innovation goals

Logical, realistic expected outcomes and deliverables

Feasible scientific and technical approaches

Predictor: Balanced ambition + feasibility.

3️⃣ Excellent, Cutting-Edge Science or Innovation

For RIA/IA/CSA or ERC-level grants, reviewers expect:

High novelty and innovation

Strong grounding in current state-of-the-art

Clear advancement beyond existing approaches

Solid theoretical or experimental foundations

Robust methodological design

Predictor: Scientific excellence is essential for competitive scoring.

4️⃣ Strong Consortium with Complementary Expertise

EC proposals are consortium-driven (except ERC/EIC Accelerator).

High-scoring consortia:

Cover all needed competencies (science, industry, policy, ethics, dissemination)

Include SMEs, industry partners, NGOs, and public bodies when relevant

Are geographically diverse across EU Member States and Associated Countries

Demonstrate strong leadership and communication structures

Predictor: Well-constructed consortium with clear roles.

5️⃣ Clear Pathway From Outputs → Outcomes → Impact

Evaluators look for a credible trajectory showing:

How research leads to specific outputs (data, tools, prototypes)

How outputs lead to uptake or use

How use produces societal, economic, scientific, or policy impact

Strong Key Performance Indicators (KPIs) and impact metrics

Predictor: Clearly mapped impact pathway.

6️⃣ Strong Implementation Plan (Work Packages, Deliverables, Gantt Chart)

Winning proposals have:

Well-designed Work Packages (WPs) with clear scope and responsibilities

Interdependencies identified and risk-mitigation strategies

Detailed milestones and deliverables

Feasible budget aligned with tasks

Strong project management plan

Predictor: High implementation quality boosts the “Excellence” and “Implementation” scores.

7️⃣ Policy Relevance and Contribution to EU Strategies

Especially critical for health, climate, digital, and social calls.

Proposals score higher when they link to:

EU Cancer Mission

EU Green Deal

Digital Europe strategy

EU Biodiversity Strategy

EU Health Union & One Health

Open Science & FAIR data mandates

Predictor: Clear alignment with EU policies.

8️⃣ Strong Stakeholder & Citizen Engagement (Especially in Social & Health Missions)

EC values inclusivity:

Patient groups

Civil society organizations

Public sector bodies

Regulatory agencies

Citizen science components

Stakeholder letters of intent or commitment strengthen credibility.

Predictor: Engagement adds impact and relevance.

9️⃣ Robust Data Management, Open Science, and Ethics

Mandatory components include:

FAIR Data Management Plan

Open access publications

Ethics self-assessment

GDPR compliance

Data security, governance, and ethical approvals

Animal-use reduction and justification (if applicable)

Predictor: Clear compliance with ethical and data obligations.

10️⃣ Well-Justified Budget and Resource Allocation

Budget must be:

Proportional to tasks

Transparent and reasonable

Efficiently distributed among partners

Free from padding or unjustified costs

Predictor: Realistic budgets improve Implementation scores.

🚫 COMMON PITFALLS THAT LEAD TO EC GRANT REJECTION