Automated Driving System (ADS) Demonstration Grants

Charging and Fueling Infrastructure (CFI) Discretionary Grant Program - Community Charging and Fueling Grants (Community Program) is sponsored by U.S. Department of Transportation (DOT) Federal Highway Administration (FHWA). This program funds the strategic deployment of publicly accessible electric vehicle charging and alternative fueling infrastructure in urban and rural communities, including downtown areas and local neighborhoods, particularly in underserved and disadvantaged communities.

HORIZON-CL5-2026-10-D6-03 funds generative AI applied to cooperative, connected and automated mobility, and it is one of the clearer statements yet of how European transport research intends to absorb large models. The scope targets robust environment perception and decision-making for Level 3 and Level 4 automated vehicle services - explicitly across the edge, on-board, at infrastructure and at back-office - with particular emphasis on vulnerable road user recognition and safety. Beyond perception, the call asks for generative AI including large language models and vision-language models to be used for scenario generation and validation testing, and for tools and guidelines that help developers integrate these technologies into existing CCAM concepts. That validation angle is the strategically interesting part: the hard unsolved problem in automated driving is proving safety across a scenario space too large to drive, and generative scenario synthesis is a direct attack on it. Proposals are expected to address security, fairness and compliance with European standards for connected and automated mobility, so a purely performance-driven proposal without regulatory and assurance work will read as incomplete. The call runs under the CCAM Partnership, which in practice means proposals should align with the partnership's roadmap and that consortia typically include vehicle manufacturers or tier-one suppliers, road operators or infrastructure partners, and research organisations. The HORIZON-CL5-2026-10 call opens on 4 June 2026 with a proposal deadline of 8 October 2026, giving roughly four months from opening - workable but tight for a consortium of this complexity, so partner discussions should begin before the call opens. Applicants should verify the topic budget, type of action and deadline on the official EU Funding and Tenders Portal.

Flagship-pilot: large-scale demonstrations of CCAM (CCAM Partnership) is sponsored by European Commission — Horizon Europe. Expected Outcome: This flagship pilot is the culmination of the entire activity catalogue carried out by the CCAM Partnership since its launch in 2021. It will combine in one project the most promising CCAM use-cases across three key domains, with the technological advancements from all its R&I clusters [1] supporting and enabling CCAM systems and services towards market uptake. This action is expected to contribute to all the following outcomes: Large-scale demonstrations of inclusive, user-oriented, and well-integrated CCAM systems and services for people and goods in mixed traffic through Field Operational Tests (FOTs), Technology Pilots, and Living Labs, building upon advanced and emerging SAE Level 2 systems to move towards SAE Level 3 and 4 functionalities, at multiple test sites and corridors showcasing CCAM potential [2] , for a minimum of 12 months. Validation of enabling technologies that facilitate the extension of Operational Design Domains (ODDs) in large-scale operations and enhance perception performance under poor lighting and adverse weather conditions in large-scale demonstrations and pilots. Assessment of deployment readiness and demonstration of technological maturity focusing on their reliability, security, and real-world applicability. Identification of the remaining technological and societal development needs to accelerate deployment and drive user and societal demand. These demonstrations will strengthen the connection with users and society through a co-creative process, ensuring that technological developments align with real-world needs and societal expectations. Recommendations for regulatory action aimed at facilitating the deployment of Automated Vehicles (AVs) in Europe, by engaging with relevant policy and regulatory bodies. Identification and selection of viable business models for each of the use-cases explored per domain, aiming for continued operation after the flagship pilot through private investment or national/local public funding including mechanisms for transferability and replicability to enable a broader application of results to other cities and regions. Scope: CCAM solutions are expected to provide a more user-centred, inclusive mobility system that enhances safety, reduces congestion, lowers harmful emissions, and contributes to decarbonization. In addition, CCAM solutions enhance transport effectiveness, thereby strengthening Europe's competitiveness in the global mobility sector. Novel mobility services can enable seamless integration with existing services such as public transport and logistics, while higher levels of automation are expected to boost transport productivity and efficiency. However, the benefits of these solutions must be proven through large-scale demonstrations, validating their effectiveness for both people and goods. It is also of key importance to integrate and test enabling vehicle technologies and to validate trusted communication and cyber security, as well as real time information transmission. Moreover, a comprehensive assessment of technology maturity is necessary, evaluating the readiness of automated driving functions within mixed traffic conditions and in confined areas. This evaluation helps determine the readiness of automation technologies for deployment, considering factors such as operational reliability, regulatory compliance, and user acceptance. By fostering a systematic approach to large-scale demonstrations, technology validation, and maturity assessment, and by prioritising zero-emission mobility, these efforts contribute to the seamless integration of CCAM solutions across the entire public and private transport ecosystem. The proposed action is expected to demonstrate different CCAM solutions and technologies in all the following domains: Individual mobility within mixed traffic environments, encompassing urban, suburban, motorway, and rural settings, with a focus on the seamless integration of automated and conventional vehicles. Programme areas: Horizon Europe (HORIZON), Global Challenges and European Industrial Competitiveness, Climate, Energy and Mobility, Industrial Competitiveness in Transport, Clean, Safe and Accessible Transport and Mobility, Smart Mobility

Generative AI for smarter CCAM: enhancing perception, decision-making, and validation (CCAM Partnership) is sponsored by European Commission — Horizon Europe. Expected Outcome: Project results are expected to contribute to all the following expected outcomes: Availability and integration of advanced, trustworthy, energy-efficient perception systems, exploiting technological advancements of Generative AI (GenAI) to enhance situational awareness and support safe decision-making; Enhanced Vulnerable Road User (VRU) safety, based on elevated, more temper-proof perception and understanding of their behaviour and intention predictions; Enhanced robustness of CCAM systems - both on-board and on the infrastructure side - in critical situations due to their training, virtual testing and validation in scenarios generated by GenAI, complementing existing scenario databases for the testing and validation of CCAM systems; Enhanced understanding of the relevance and limitations of using GenAI for CCAM; Tools and harmonised approaches for the use of GenAI in mobility technology development, training and validation, as well as for systemic applications such as traffic management and remote control, integrating them into existing approaches. Scope: Pilots and demonstrations using Level 3 and 4 vehicle services face major challenges in perception and decision making, highlighting the necessity for low-latency solutions that enhance responsiveness and situational awareness in real-time operating conditions. This is especially relevant for driving in more complex environments like urban areas, where environmental variance is higher and where new scenarios can be regularly encountered. Furthermore, there is the need to limit the latency, bandwidth and energy use for on-board calculations, as well as the need to enhance the security, privacy and reliability (e.g. scene understanding and prediction of near-future scenario development). For rapid decision-making in interactions with VRUs, this is essential for implementing CCAM-enabled solutions and ensuring scalability. At the same time, developments of sector-agnostic technologies show advancements -such as GenAI- that can be beneficial for CCAM. First exploratory steps can be expected from a project funded under HORIZON-CL5-2023-D6-01-02 [1] regarding the potential in the virtual generation of edge cases, which could be used for the development, training, virtual testing and validation of CCAM systems. Further advancements in GenAI applications specifically for the CCAM domain need to be developed, trained and validated [2] . Thus, proposed actions shall include approaches to exploit further technological advancements for CCAM. Major steps are needed to advance to highly advanced, ultra-safe, trustworthy and energy efficient real-time perception and decision-making systems for automated vehicles, specifically focusing on scalable solutions and the exploitation of GenAI. These advancements should leverage low latency systems or distributed computing resources to facilitate real-time processing, thereby improving system responsiveness and safety. This topic will thus contribute to the AI Continent Action Plan [3] by fostering AI development and adoption in the automotive sector. Proposed actions are expected to address all the following aspects: Development of tools and approaches for robust environment perception and decision making (at the edge, on-board, at infrastructure or back-office). These approaches shall aim at accelerating and advancing the reasoning of decision making, increasing the level of efficiency, (cyber)-security and reliability of the applications, with path planning as initial use case. This is to support amongst others the perception of VRUs, the prediction of their behaviour and their intentions, and includes data sharing approaches for CCAM solutions to create a larger time window for actions in near accident scenarios. The use of advanced GenAI, including Large Language Models (LLMs), Vision Language Models (VLMs) or Vision Language Action (VLAs) can significantly enhance these capabilities by leveraging their advanced contextual Programme areas: Horizon Europe (HORIZON), Global Challenges and European Industrial Competitiveness, Climate, Energy and Mobility, Industrial Competitiveness in Transport, Clean, Safe and Accessible Transport and Mobility, Smart Mobility