European UnionNon-profit, Private Business, Research Institution, Public Institution
Predicting and avoiding road crashes based on Artificial Intelligence (AI) and big data
HORIZON-CL5-2026-01-D6-14
Funding Amount
€5.0M - €5.0M
Deadline
20/01/2026
Eligible Organization Types
Non-profit, Private Business, Research Institution, Public Institution
Trusted by
What is the Predicting and avoiding road crashes based on Artificial Intelligence (AI) and big data Call?
Grant Description
This call seeks research and innovation actions to develop an artificial intelligence-enabled digital twin of road traffic and infrastructure for proactive crash prediction and avoidance. It addresses the need to shift from reactive to proactive road safety management by integrating and analysing real-time and historical data from multiple sources to identify safety-critical situations and deploy preventive countermeasures in real time, ensuring fair, bias-free interventions and interoperability...
Required Results for Successful Funding
Expected Outcomes
• Availability of knowledge on high-risk locations along road networks before crashes occur, enabling proactive deployment of countermeasures by road authorities.
• Predictive identification of safety-critical situations using multi-source data, facilitating real-time interventions to avoid crashes.
• Methodology for determining optimal sample sizes to ensure reliable real-time crash occurrence prediction.
• Enhanced traffic flow monitoring and incorporation of flow variations in real-time crash prediction, leading to more effective traffic management and resilience against unexpected events.
• Predictive identification of safety-critical situations using multi-source data, facilitating real-time interventions to avoid crashes.
• Methodology for determining optimal sample sizes to ensure reliable real-time crash occurrence prediction.
• Enhanced traffic flow monitoring and incorporation of flow variations in real-time crash prediction, leading to more effective traffic management and resilience against unexpected events.
Funded areas
Scope Requirements
Proposals must address all of the following:
• Develop an AI-enabled digital twin of traffic and infrastructure integrating multi-source data (historical, current, forecast, crowdsourced, sensors, environmental, infrastructure) and explore additional data types (sociodemographic, economic, behavioural, security cameras, event/tourism data).
• Analyse in detail the technical challenges in acquiring, validating and fusing big data from diverse road transport system sensors.
• Create methods and tools for real-time and historical data-driven prediction of safety-critical situations at quantifiable risk levels, including optimal sample size determination.
• Ensure AI models are free from bias and provide fair, non-discriminatory safety improvements for all road users.
• Analyse ethical, legal and economic issues around large-scale data collection and sharing; develop concepts to address privacy, data ownership and organisational barriers.
• Identify feasible real-time countermeasures to reduce instantaneous risk, complementary to existing ITS services.
• Demonstrate feasibility through pilots or case studies.
• Build stakeholder consensus on deployment routes, establishing interoperability and data-sharing standards in line with FAIR principles and leveraging Common European Data Spaces.
• Explore use of complementary metadata (e.g., crash databases) and links to European data space initiatives.
• Develop recommendations for updating relevant technical standards and EU legal frameworks.
• Include international cooperation, particularly with US, Japan, Singapore, Australia, and leverage cross-modal experience.
• Develop an AI-enabled digital twin of traffic and infrastructure integrating multi-source data (historical, current, forecast, crowdsourced, sensors, environmental, infrastructure) and explore additional data types (sociodemographic, economic, behavioural, security cameras, event/tourism data).
• Analyse in detail the technical challenges in acquiring, validating and fusing big data from diverse road transport system sensors.
• Create methods and tools for real-time and historical data-driven prediction of safety-critical situations at quantifiable risk levels, including optimal sample size determination.
• Ensure AI models are free from bias and provide fair, non-discriminatory safety improvements for all road users.
• Analyse ethical, legal and economic issues around large-scale data collection and sharing; develop concepts to address privacy, data ownership and organisational barriers.
• Identify feasible real-time countermeasures to reduce instantaneous risk, complementary to existing ITS services.
• Demonstrate feasibility through pilots or case studies.
• Build stakeholder consensus on deployment routes, establishing interoperability and data-sharing standards in line with FAIR principles and leveraging Common European Data Spaces.
• Explore use of complementary metadata (e.g., crash databases) and links to European data space initiatives.
• Develop recommendations for updating relevant technical standards and EU legal frameworks.
• Include international cooperation, particularly with US, Japan, Singapore, Australia, and leverage cross-modal experience.
Additional Conditions for Applicants
Special Conditions
• Type of action: Horizon Research and Innovation Action under a Lump Sum Grant Model (MGA type: HORIZON-AG-LS).
• Single-stage procedure, opening 16 September 2025, deadline 20 January 2026 at 17:00 Brussels time.
• Indicative budget: EUR 30 million; expected to fund approximately three projects (~EUR 10 million lump sum each).
• Consortia must comply with EU eligibility rules for Horizon Europe (minimum consortium composition, eligible countries).
• Projects must adhere to GDPR and relevant EU data governance regulation.
• All data and tools produced should follow FAIR principles and be interoperable with Common European Data Spaces.
• Applicants should consider results of previous projects (e.g., OMICRON) and engage stakeholders early.
• International cooperation is strongly encouraged, notably with US, Japan, Singapore, Australia.
• Projects must include ethics self-assessment and address ethical issues prior to grant agreement.
• Single-stage procedure, opening 16 September 2025, deadline 20 January 2026 at 17:00 Brussels time.
• Indicative budget: EUR 30 million; expected to fund approximately three projects (~EUR 10 million lump sum each).
• Consortia must comply with EU eligibility rules for Horizon Europe (minimum consortium composition, eligible countries).
• Projects must adhere to GDPR and relevant EU data governance regulation.
• All data and tools produced should follow FAIR principles and be interoperable with Common European Data Spaces.
• Applicants should consider results of previous projects (e.g., OMICRON) and engage stakeholders early.
• International cooperation is strongly encouraged, notably with US, Japan, Singapore, Australia.
• Projects must include ethics self-assessment and address ethical issues prior to grant agreement.
Important dates
Open from: 16/09/2025
Deadline:20/01/2026
Win your grant with Cogrant
How cogrant works
Smart Funding Fit Check
Instantly know if your project fits call criteria. Our system highlights strengths, gaps, and risks, so you only apply when your chances are real.
Intelligent Research Engine
Our research engine digs deeper than any founder could alone. It pulls data about your company, your market, and even your competitors - giving your proposal an edge backed by facts, not fluff.
Full Proposal Development
Our system builds your entire proposal from start to finish, ensuring every section is complete, coherent, and crafted to the highest quality. You focus on your vision — we deliver the winning document.
Expert Review
Our experts run a complete review before delivery, ensuring your draft proposal is accurate, well-structured, and polished. You’ll receive a high-quality draft that’s ready for your final touches before submission.
Our Numbers
Results that speak for themselves
70+
proposals prepared
€36.7M
total funding secured for clients
65%
win rate in highly competitive calls