Loading…
Strategic Analysis
A winning proposal must position Generative AI (LLMs, VLMs, VLAs) not merely as a novelty, but as a robust, safe, and verifiable toolchain for perception, contextual reasoning, and synthetic edge-case scenario generation in CCAM systems up to TRL 6. The strategic differentiator lies in balancing advanced reasoning (especially for Vulnerable Road Users) with strict trustworthiness, energy efficiency at the edge, and integration into the European Software-defined Vehicle (SDV) ecosystem. Crucially, addressing AI hallucinations, bias, and deterministic safety validation will convince evaluators that GenAI is ready for real-world automotive adoption.
TRL 2 → 6
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 reasoning and pattern recognition. Furthermore, GenAI can complement existing perception systems by improving sensory input interpretation and providing enriched environmental contexts, which enhance decision-making and adaptability.
Scenario generation of interactions of CCAM enabled vehicles with other road users, which is essential for advances in validation and testing, extending existing datasets and scenarios as GenAI can, based on existing data, deliver variations of scenarios (e.g. cultural differences of road users and infrastructure variability.)
Integration of GenAI technologies into existing approaches (development, training and validation) for their further enrichment. Understanding the limits of using GenAI technologies as well as the benefits and develop guidelines for valid approaches for this integration (including consideration of gender biases and fairness to ensure AI systems are transparent and accountable) and providing an outlook on the uptake of the tools and approaches developed can be done for a variety of CCAM components and technologies, as well as for systemic applications such as traffic management and remote control.
Encouraging collaboration with the European Software-defined Vehicle (SDV) initiative by adopting existing interfaces and building blocks, and proposing new ones developed within the project for potential inclusion in the SDV framework.
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.
Improved mobility for people and goods in all weather conditions, ensuring safe, shared, inclusive, affordable, attractive, and accessible door-to-door mobility, for private and public transport in mixed traffic and confined areas, as well as open roads.
Seamless integration of CCAM solutions into existing transport ecosystems to ensure interoperability, promote multimodality, enhance traffic safety, catering to diverse user needs and behaviours.
Resilient, climate-neutral, and sustainable mobility solutions with reduced carbon footprints, resulting in greener, less congested, cost-effective, and demand-responsive transport systems.
Increased competitiveness of the transport system using secure and hyper-advanced technologies such as real-time perception, situational awareness, and decision-making systems, based on trustworthy Artificial Intelligence (including Edge and Generative AI), satellite navigation, smart traffic management, and tools for software development for CCAM applications.
Enhanced resilience of transport networks through improved operational efficiency for both passenger and intermodal freight transport, future-proofed mobility systems supporting EU competitiveness while ensuring affordable and accessible transport for all passengers.
Drastic reduction in road fatalities for all types of users, especially on rural areas
Improved resilience of the public transport system via the use of AI
Advanced technologies and methods for improved reliability in complex environments for aviation
AI Continent Action Plan
highWhile not a single formal EU policy document, the 'AI Continent Action Plan' broadly refers to the EU's strategic ambition to foster AI development and adoption, positioning Europe as a global leader in trustworthy and human-centric AI. This encompasses initiatives like the Coordinated Plan on Artificial Intelligence, the AI Act, and efforts to build international partnerships and strengthen Europe's AI ecosystem.
Proposals should demonstrate how their AI integration efforts align with the EU's strategic objectives for AI leadership, ethical development, and societal benefit. This includes addressing aspects of trustworthiness, transparency, accountability, and potentially contributing to international cooperation or the global competitiveness of European AI.
European Automotive Action Plan
mediumThe European Automotive Action Plan (historically rooted in CARS 2020 and updated strategic industrial roadmaps) outlines strategic priorities to maintain a competitive, sustainable, and connected automotive sector in the EU. It focuses on accelerating the transition to clean mobility, connected and automated driving (CCAM), and ensuring digital sovereignty in vehicle technologies.
Evaluators expect proposals to align with EU industrial competitiveness objectives for connected and automated mobility (CCAM). This includes demonstrating how generative AI solutions strengthen the European automotive value chain, support vehicle safety and digital transformation, and overcome industrial validation and testing bottlenecks.
Specific requirements
EU space data infrastructures
If the project uses satellite-based Earth observation, positioning, navigation or timing data/services, beneficiaries must use Copernicus and/or Galileo/EGNOS. Other sources may be added but not substitute EU infrastructures.
Civil applications only
Horizon Europe funds exclusively civil applications. Research with exclusive military or dual-use application is excluded.
Gender Equality Plan
Having a Gender Equality Plan (GEP) is an eligibility criterion for public bodies, research organisations, and higher education institutions from Member States and Associated Countries.
Open Science
Mandatory open access to peer-reviewed scientific publications and responsible management of research data (FAIR principles, DMP required).
">
described in Annex A and Annex E of the Horizon Europe Work Programme General Annexes.
Proposal page limits and layout: described in Part B of the Application Form available in the Submission System.
described in Annex B of the Work Programme General Annexes.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon Europe projects. See the information in the Horizon Europe Programme Guide.
If projects use satellite-based earth observation, positioning, navigation and/or related timing data and services, beneficiaries must make use of Copernicus and/or Galileo/EGNOS (other data and services may additionally be used).
Subject to restrictions for the protection of European communication networks.
described in Annex B of the Work Programme General Annexes.
described in Annex C of the Work Programme General Annexes.
are described in Annex D of the Work Programme General Annexes.
are described in Annex F of the Work Programme General Annexes and the Online Manual.
described in Annex F of the Work Programme General Annexes.
The granting authority may, up to 4 years after the end of the action, object to a transfer of ownership or to the exclusive licensing of results, as set out in the specific provision of Annex 5.
Eligible costs will take the form of a lump sum as defined in the Decision of 7 July 2021 authorising the use of lump sum contributions under the Horizon Europe Programme – the Framework Programme for Research and Innovation (2021-2027) – and in actions under the Research and Training Programme of the European Atomic Energy Community (2021-2025) [[This decision is available on the Funding and Tenders Portal, in the reference documents section for Horizon Europe, under ‘Simplified costs decisions’ or through this link: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ls-decision_he_en.pdf]].
described in Annex G of the Work Programme General Annexes.
Not applicable.
Application form templates — the application form specific to this call is available in the Submission System
Standard application form (HE RIA, IA)
Evaluation form templates — will be used with the necessary adaptations
Standard evaluation form (HE RIA, IA)
Guidance
Model Grant Agreements (MGA)
Call-specific instructions
Guidance: "Lump sums - what do I need to know?"
HE Main Work Programme 2026-2027 – 1. General Introduction
HE Main Work Programme 2026-2027 – 8. Climate, Energy and Mobility
HE Main Work Programme 2026-2027 – 15. General Annexes
HE Framework Programme 2021/695
HE Specific Programme Decision 2021/764
EU Financial Regulation 2024/2509
Decision authorising the use of lump sum contributions under the Horizon Europe Programme
Rules for Legal Entity Validation, LEAR Appointment and Financial Capacity Assessment
EU Grants AGA — Annotated Model Grant Agreement
Funding & Tenders Portal Online Manual
Evaluators will prioritize proposals that clearly demonstrate compliance with the Lump Sum model (work packages defined with clear, verifiable milestones and outputs) and respect the 45-page limit for lump-sum RIA topics. Key evaluation hooks include: (1) Concrete integration with the European SDV initiative via open APIs/interfaces; (2) Trustworthy AI compliance (transparency, bias mitigation, EU AI Act alignment); (3) Demonstrable safety improvements for Vulnerable Road Users (VRUs); and (4) Conditional compliance ensuring that any positioning or timing data mandates Galileo/EGNOS utilization.
Everything the call asks for, seen from the call's point of view. Each line shows what answers it, and which partner carries it.
This matrix lists everything the call asks for: outcomes, impacts, scope, the requirements buried in the call text, and policy alignment. Sign up free and GrantForge tracks each line against the concept you build.
| Requirement | Covered by | Carried | Status |
|---|---|---|---|
| Scope activities | |||
| SC1Development 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 reasoning and pattern recognition. Furthermore, GenAI can complement existing perception systems by improving sensory input interpretation and providing enriched environmental contexts, which enhance decision-making and adaptability. | · | · | Sign up to track |
| SC2Scenario generation of interactions of CCAM enabled vehicles with other road users, which is essential for advances in validation and testing, extending existing datasets and scenarios as GenAI can, based on existing data, deliver variations of scenarios (e.g. cultural differences of road users and infrastructure variability.) | · | · | Sign up to track |
| SC3Integration of GenAI technologies into existing approaches (development, training and validation) for their further enrichment. Understanding the limits of using GenAI technologies as well as the benefits and develop guidelines for valid approaches for this integration (including consideration of gender biases and fairness to ensure AI systems are transparent and accountable) and providing an outlook on the uptake of the tools and approaches developed can be done for a variety of CCAM components and technologies, as well as for systemic applications such as traffic management and remote control. | · | · | Sign up to track |
| SC4Encouraging collaboration with the European Software-defined Vehicle (SDV) initiative by adopting existing interfaces and building blocks, and proposing new ones developed within the project for potential inclusion in the SDV framework. | · | · | Sign up to track |
| Expected outcomes | |||
| EO1Availability 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; | · | · | Sign up to track |
| EO2Enhanced Vulnerable Road User (VRU) safety, based on elevated, more temper-proof perception and understanding of their behaviour and intention predictions; | · | · | Sign up to track |
| EO3Enhanced 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; | · | · | Sign up to track |
| EO4Enhanced understanding of the relevance and limitations of using GenAI for CCAM; | · | · | Sign up to track |
| EO5Tools 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. | · | · | Sign up to track |
| Other requirements | |||
| REQ1Collaboration with the European Software-defined Vehicle (SDV) initiativeProjects should encourage collaboration with the European Software-defined Vehicle (SDV) initiative by adopting existing interfaces and building blocks, and proposing new ones developed within the project for potential inclusion in the SDV framework. | · | · | Sign up to track |
| REQ2Coordination with European Connected and Autonomous Vehicle Alliance (ECAVA)Proposed actions should include measures to ensure close coordination with the European Connected and Autonomous Vehicle Alliance (ECAVA) announced in the European Automotive Action Plan. | · | · | Sign up to track |
| REQ3KPI reporting to the CCAM European PartnershipAs this topic implements the co-programmed European Partnership on CCAM, projects are expected to report results to the CCAM Partnership in support of the monitoring of its KPIs. | · | · | Sign up to track |
| REQ4Liaison with the ADRA PartnershipProjects funded under this topic are expected to liaise with the AI, Data and Robotics Association (ADRA) Partnership. | · | · | Sign up to track |
| REQ5Mandatory use of Copernicus and/or Galileo/EGNOS servicesIf projects use satellite-based earth observation, positioning, navigation and/or related timing data and services, beneficiaries must make use of Copernicus and/or Galileo/EGNOS. | · | · | Sign up to track |
| REQ6Mitigation of gender biases and fairness in AI integrationGuidelines for GenAI integration must include consideration of gender biases and fairness to ensure AI systems are transparent and accountable. | · | · | Sign up to track |
| Expected impacts | |||
| EI1Improved mobility for people and goods in all weather conditions, ensuring safe, shared, inclusive, affordable, attractive, and accessible door-to-door mobility, for private and public transport in mixed traffic and confined areas, as well as open roads. | · | · | Sign up to track |
| EI2Seamless integration of CCAM solutions into existing transport ecosystems to ensure interoperability, promote multimodality, enhance traffic safety, catering to diverse user needs and behaviours. | · | · | Sign up to track |
| EI3Resilient, climate-neutral, and sustainable mobility solutions with reduced carbon footprints, resulting in greener, less congested, cost-effective, and demand-responsive transport systems. | · | · | Sign up to track |
| EI4Increased competitiveness of the transport system using secure and hyper-advanced technologies such as real-time perception, situational awareness, and decision-making systems, based on trustworthy Artificial Intelligence (including Edge and Generative AI), satellite navigation, smart traffic management, and tools for software development for CCAM applications. | · | · | Sign up to track |
| EI5Enhanced resilience of transport networks through improved operational efficiency for both passenger and intermodal freight transport, future-proofed mobility systems supporting EU competitiveness while ensuring affordable and accessible transport for all passengers. | · | · | Sign up to track |
| EI6Drastic reduction in road fatalities for all types of users, especially on rural areas | · | · | Sign up to track |
| EI7Improved resilience of the public transport system via the use of AI | · | · | Sign up to track |
| EI8Advanced technologies and methods for improved reliability in complex environments for aviation | · | · | Sign up to track |
| Underlying policies | |||
| POL1ai continent action planWhile not a single formal EU policy document, the 'AI Continent Action Plan' broadly refers to the EU's strategic ambition to foster AI development and adoption, positioning Europe as a global leader in trustworthy and human-centric AI. This encompasses initiatives like the Coordinated Plan on Artificial Intelligence, the AI Act, and efforts to build international partnerships and strengthen Europe's AI ecosystem. | · | · | Sign up to track |
The binding rules of this call. Items marked auto are verified by GrantForge from the call and the template. The others are yours to confirm.
LMIC entities auto-eligible
Low/middle-income country entities are automatically eligible for funding.
EU space data infrastructures
If the project uses satellite-based Earth observation, positioning, navigation or timing data/services, beneficiaries must use Copernicus and/or Galileo/EGNOS. Other sources may be added but not substitute EU infrastructures.
Civil applications only
Horizon Europe funds exclusively civil applications. Research with exclusive military or dual-use application is excluded.
Gender Equality Plan
Having a Gender Equality Plan (GEP) is an eligibility criterion for public bodies, research organisations, and higher education institutions from Member States and Associated Countries.
Open Science
Mandatory open access to peer-reviewed scientific publications and responsible management of research data (FAIR principles, DMP required).
4 key insights you must internalise before writing. Each is grounded in the call text and tells you what evaluators will actually look for. Share these with your consortium before drafting.
Because this is a Lump Sum RIA, evaluators will heavily scrutinize the work package architecture for clear, verifiable milestones that trigger payments. Furthermore, applicants must strictly adhere to the 45-page limit, requiring a highly concise narrative that balances deep technical AI architecture with the mandatory lump-sum budget justifications.
Source: Evaluation criteria (pre-award)
The call explicitly mandates that proposals address all listed scope aspects, meaning you cannot specialize solely in perception or scenario generation. A winning consortium must integrate edge-case scenario generation, VRU perception, bias mitigation guidelines, and European SDV collaboration into a single cohesive TRL 6 pipeline.
Source: Scope
Evaluators are explicitly instructed to look for concrete integration with the European SDV initiative via open APIs and interfaces. Additionally, treating AI hallucinations and bias is not just a technical challenge but a compliance requirement; proposals must demonstrate strict alignment with the EU AI Act to secure top scores in trustworthiness.
Source: Evaluation criteria (pre-award)
If your CCAM perception and decision-making system relies on any satellite-based positioning, navigation, or timing data, it is an eligibility requirement to utilize Copernicus and/or Galileo/EGNOS. Failing to explicitly state and architect this integration will trigger a conditional compliance failure during the evaluation.
Source: Eligibility rules
Talk to the Grant Coach to build your concept. There is no set order: start wherever your project starts. The sections below mirror what the conversation produces, and your coverage tracks the progress. You can refine everything once your project workspace is created.
The problems this call frames, and who they affect. Your concept and plan address them.
Current CCAM perception systems struggle with high latency, occlusion, and interpreting nuanced human intent (e.g., eye contact, hesitation) in complex urban environments, causing safety-critical reaction delays.
Physical testing cannot capture the long tail of hazardous, rare, or culturally diverse driving scenarios, while existing synthetic datasets lack high-fidelity behavioral realism and environmental variance.
Generative AI foundational models are computationally heavy, prone to hallucinations, and lack formal verification guarantees, hindering compliance with automotive functional safety (ISO 26262) and the EU AI Act.
Vehicle manufacturers and component suppliers adopting advanced software-defined vehicle architectures, onboard edge computing, and AI-driven ADAS/CCAM perception stacks.
Road users directly exposed to urban traffic whose physical safety depends on automated vehicles accurately predicting their trajectory and subtle behavioral cues.
Public and private bodies managing infrastructure-to-vehicle (I2V) connectivity, back-office teleoperation, and smart traffic control centers.
Researchers in AI, automotive engineering, robotics, and formal verification advancing safe machine learning, edge computing, and mobility benchmarks.
Entities such as ISO, UNECE, CEN/CENELEC, and EU regulatory bodies defining certification standards for automotive AI safety and trustworthiness.
The long-term impacts your project should drive, and the policies they serve.
Substantial decrease in urban pedestrian and cyclist collisions achieved through early intent recognition and larger defensive action time windows by CCAM vehicles.
Strengthened market position of European Tier-1s and OEMs through standardized, modular GenAI middleware integrated into the European Software-defined Vehicle architecture.
Open benchmark datasets, synthetic scenario libraries, and explainable AI metrics adopted by international research groups for rigorous safety auditing.
Reduced vehicular idling and smoother automated driving trajectories driven by predictive path planning, coupled with low-power edge-AI inference architectures.