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Challenge 4: Generative AI for Automatic Test Case Generation from Crash Databases & Standards

Description

This challenge aims to develop Generative AI and multimodal AI solutions that transform fragmented European accident and safety data into structured, explainable and validation-ready scenario knowledge for automated driving. The objective is to automate the extraction, enrichment and matching of traffic scenarios from crash databases and standards, enabling more efficient simulation-based validation and improving safety assessment for automated driving systems.

Funding Amount

The AI-BOOST competition follows a two-stage prize model.

SPARK Phase

  • 5 winners receive €28,500 each.
  • Prize paid in two instalments:
    • 50% after signing the Participation Agreement.
    • 50% after successfully completing the Mid-Term Checkpoint.

ADVANCE Phase

  • The five SPARK winners continue into the Advance Phase.
  • One overall Challenge Winner receives €100,000.

Special Awards

  • Innovation Excellence Award: €12,500
  • Responsible AI Award: €12,500

(A participant may receive both the Challenge Prize and a Special Award.)

 

Submission Deadlines

  • Call opens: 3 July 2026
  • Application deadline: 8 September 2026 (17:00 CEST)

Submission Conditions

Applications must be submitted through the AI-BOOST F6S platform by 8 September 2026 (17:00 CEST). Proposals must address the Challenge 4 objectives and comply with the AI-BOOST programme requirements.

  • Applications must be completed in English.
  • Only one application per applicant is permitted.
  • Solutions should focus on AI-driven scenario extraction, enrichment and validation for automated driving.
  • Projects must comply with the EU AI Act, GDPR and Responsible AI principles.
  • External AI models, APIs or cloud services used must be disclosed.
  • Solutions must ensure explainability, traceability and reproducibility, and must not transmit personally identifiable information (PII) or sensitive accident data to external services.
  • Selected participants must complete the SPARK and ADVANCE phases, mentoring activities, Mid-Term Checkpoint and Final Live Competition.

Eligible Businesses

The competition is open to organisations and innovators capable of developing AI solutions for mobility, automotive engineering and safety validation, including:

  • SMEs and start-ups
  • Large companies
  • Universities and higher education institutions
  • Research organisations
  • NGOs and non-profit organisations
  • Individual innovators
  • Single legal entities
  • Consortia

Eligible Countries

Applicants must be established in:

  • EU Member States
  • Horizon Europe Associated Countries
  • EU Overseas Countries and Territories (OCTs)

Technology Readiness Level (TRL)

  • Expected starting TRL: TRL 3 (Proof of Concept)
  • Target TRL: TRL 5
  • Solutions should demonstrate validation in relevant industrial environments and integration potential into automated driving validation workflows.

Expected Outcome

Projects are expected to deliver:

  • AI methods for extracting Functional Scenarios from heterogeneous accident datasets.
  • AI methods for enriching scenarios using multimodal evidence while handling uncertainty.
  • Structured Scenario Representations with traceable and explainable outputs.
  • Methods for matching accident-derived scenarios to validation scenario catalogues and identifying coverage gaps.
  • Where applicable, simulation-ready scenario descriptions compatible with OpenSCENARIO or similar formalisms, including visualisation and simulation support.
  • Proof of technical feasibility and integration potential for industrial validation workflows.

Scope

Projects should develop AI-driven methods capable of transforming heterogeneous accident and safety evidence into structured scenario representations for automated driving validation.

The solution should be able to:

  • Extract relevant information from accident datasets, crash reports and related evidence.
  • Identify and harmonise recurring Functional Scenarios across heterogeneous data sources.
  • Enrich Functional Scenarios into Logical Scenarios using multimodal evidence (e.g. text, images, maps and infrastructure data).
  • Generate a Structured Scenario Representation with traceable evidence and confidence information.
  • Match accident-derived scenarios against validation scenario catalogues (e.g. Euro NCAP) to assess validation coverage and identify gaps.
  • Support either individual benchmark components or an integrated end-to-end workflow.

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