Test Scenario

SUNRISE

• Funder: European Union
• Partners: TNO, Siemens, ERTICO, TOYOTA, VEDECOM, Renault, Continental, Vicomtech, CVC, IDIADA, Chalmers, RISE, bast, IKA, Deepen, Infineon, AVL, Virtual Vehicle, Universita Di Trento, ICCS

Project Context and Challenge:

SUNRISE stands for Safety Assurance Framework for Connected and Automated Mobility Systems. The main goal of this project is to develop a harmonised and scalable CCAM Safety Assurance Framework that fulfils the needs of different automotive stakeholders. CCAM stands for Cooperative, Connected and Automated Mobility. This safety assurance framework plays a key role in the project. As shown in the figure below, different stakeholders each have their own specific interest in this safety assurance framework. For example, developers of CCAM systems, but also the entities that certify these systems or that perform research in this field, and even citizens, might have their own specific interest in this safety assurance framework.

At a high level, there are three main components of the safety assurance framework: audit, in-service monitoring and reporting (or ISMR), and performance assurance. Performance assurance focuses on ensuring that the system can perform as intended within its defined Operational Design Domain (ODD), which can be visualised as a set of operating conditions within which the system is designed to operate safely. The audit focuses on ensuring the development process, the tools used, and the management system are adequate. The ISMR focuses on capturing and recording any additional information during the system's deployment for consideration in the system’s future design. All components are part of the safety assurance framework; however, in SUNRISE, we focus on performance assurance using a scenario-based approach and incorporate a virtual environment. Please note that we also have the input layer to the safety assurance framework, which includes the requirements containing ODD and behaviour, as well as other external requirements and test objectives.

The performance assurance workflow comprises three main blocks: scenario, execution, and analysis.

1. Scenario Block: This involves creating, formatting, and storing scenarios. Scenarios can be created using knowledge-based (expert insights) or data-based (e.g., UK STATS 19 dataset) approaches. Scenarios are formatted for different stakeholders using common formats like the ASAM OpenSCENARIO/OpenDRIVE and BSI Flex 1889 and stored in a database such as the Safety Pool^TMScenario database.
2. Execution Block: Concrete scenarios are obtained and ready for execution, combined with test objectives, and allocated to various testing environments. The framework supports virtual, simulated, and real-world testing environments.
3. Analyse Block: Includes coverage analysis and test evaluation. Coverage analysis assesses scenario coverage from multiple perspectives and explores the scenario parameter spaces iteratively to identify failures. Test evaluation checks if individual tests meet criteria like speed limits and collision avoidance. The combined analysis results determine the system's overall safety assurance.

SYNERGIES: Real and synthetic scenarios generated for the development, training, virtual testing and validation of CCAM systems

• Funding value: €18,636,667.60, funded by EU Horizon (£1,064,591to WMG)

• Partners: BMW (DE), IDIADA, Renault (FR), Stellantis (FR), Toyota Motor Europe (BE), AVL (AT), DLR (DE), TNO (NL), University of Genoa (IT), VICOMTech (ES), Virtual Vehicle Research (AT), VUFO Dresden (DE), and University of Warwick (UK)

Project Context and Challenge:

Connected, Cooperative, and Automated Mobility (CCAM) faces major challenges due to fragmented and non-interoperable scenario databases, lengthy development cycles, and regulatory uncertainty. Current validation methods are inefficient, hindering the safe deployment of automated driving systems. SYNERGIES addresses these challenges by building on previous EU initiatives (HEADSTART and SUNRISE) to deliver a harmonised, federated European Scenario Dataspace, enabling scenario-based testing, safety assurance, and data-driven innovation in CCAM systems.

Objectives:

SYNERGIES aims to accelerate the development, training, and validation of CCAM systems through an interoperable, European-wide Scenario Dataspace and Marketplace. The project will:

1.Federate existing and new scenario databases into a unified, dynamic platform ensuring scalable data access.

2.Extend the range of real and synthetic scenarios, including rural, urban, and edge-case environments.

3.Develop AI-based tools for semi-automatic data processing, annotation, and scenario extraction.

4.Establish governance frameworks and business models to ensure sustainability, security, and standardisation.

5.Facilitate broad stakeholder engagement, from industry to regulators and researchers, to promote adoption and interoperability across Europe.

The overall goal is to provide a trusted, user-centred, and open infrastructure supporting continuous validation and regulatory compliance for CCAM technologies.

Approach and Innovation:

• SYNERGIES adopts a federated, AI-driven, and iterative approach to build a scalable Scenario Dataspace. It integrates lessons from previous EU projects (SUNRISE, Hi-Drive, FAME) and leverages existing scenario sources (Safety Pool™, ADScene, StreetWise, L3Pilot). The project introduces a Federated Layer Architecture to connect distributed databases securely while ensuring ownership sovereignty and interoperability. AI and generative methods will enhance scenario diversity, covering complex, rare, and adverse conditions. Governance models and open APIs will facilitate cross-sector collaboration. The platform’s Marketplace will provide stakeholders with tools for data processing, scenario generation, and testing. By combining semantic technologies, trustworthy AI, and standardisation input (ISO, ASAM, UNECE), SYNERGIES creates an integrated framework for scenario-based validation and certification of CCAM systems.

Impact and Next Steps:

SYNERGIES will accelerate Europe’s transition toward safe, efficient, and sustainable automated mobility by reducing testing costs, shortening development cycles, and enhancing safety assurance. The project will reinforce EU leadership in CCAM technologies, contributing to international standards and UNECE regulations. Its long-term legacy includes a continuously updated European Scenario Dataspace, an active stakeholder network, and a marketplace for data-driven innovation. By fostering interoperability, trust, and data sovereignty, SYNERGIES will lay the foundation for a harmonised European CCAM validation ecosystem and future large-scale deployments.

ReliablE in-Vehicle pErception and decisioN-making in complex environmenTal conditions (EVENTS)

• Funding value: Total €6M+, Value to WMG €650k

• Partners: I-SENSE Research Group, of the Institute of Communication and Computer Systems (ICCS), Institute of Measurement, Control and Microtechnology at Ulm University (UULM), SEAbility, Tecnalia, Perciv AI, HITACHI France, APTIV, HITACHI UK, STELLANTIS, TU Delft

Project Context and Challenge:

Driving is a challenging task that involves complex situations that need to be handled safely and efficiently. The same is valid for Connected and Automated Vehicles (CAVs), which also need to handle these situations, to a certain extent, depending on their automation level and their target Operational Design Domain (ODD). The higher the automation level is, the higher the (user) expectations for the system to cope with these situations are. In the context of this project, these complex situations where the normal operation of the CAV is close to being disrupted (e.g. when the ODD limit is reached due to traffic changes, harsh weather or light conditions, unstructured roads, imperfect data, sensor or communication failures, etc.), are called "events." Within the scope of this project, these events are clustered under three main use cases:

• Interaction with VRUs (vulnerable road users) in Complex Urban Environments

• Non-standard and Unstructured Road Conditions

• Low Visibility and Adverse Weather Conditions

Project Objectives:

The vision of EVENTS is to create a robust and resilient perception and decision-making system for CAVs to manage various types of “events”. EVENTS will allow overcoming the current CAV ODD limitations due to the dynamically changing road environment (VRUs, obstacles) and/or imperfect data (e.g., sensor and communication failures) while ensuring continuous and safe operation. In EVENTS, if the system or some of the subsystems cannot perform with the expected quality and reliability, an improved minimum risk manoeuvre is triggered.

Approach and Innovation:

WMG has been heavily involved in the Vehicle System Hazard Analysis & Risk Assessment (HARA) through the application of System Theoretic Process Analysis (STPA) for Safety of the intended Functionality (SOTIF) and the development of self-assessment for Perception & Localisation systems of this project. We have also been leading the Standardisation & International Liaison activities (ISO TC 204 WG14 (Guidance on ODD), ISO TC22 SC32 Working Groups 13&14, ASAM: OpenODD (Project Lead), ASAM: OSI performance (Work Package Lead), BSI: PAS 1883 (Technical Lead) and BSI: Flex 1889 (Technical Lead)).

Impacts and Next Steps:

EVENTS will improve technological advances in perception and decision-making for real-time CAV operation, paving the way for safe deployment of CAVs. The purpose of Standardisation & International Liaison task is to centrally coordinate all inputs arising from EVENTS across various standardisation bodies and organisations. Since international collaboration is essential for the safe deployment of automated driving, this task will also ensure alignment at both European and global levels. This includes maintaining liaisons with research initiatives, policymakers, organisations, associations, technical communities, and networks in regions such as Europe, the USA, Japan, South Korea, China, and Canada.

Safety Pool™ Scenario Database

Context and Challenge:

The safe deployment of Connected, Cooperative, and Automated Mobility (CCAM) systems depends on robust, scalable, and standardised scenario-based testing. However, fragmented tools, limited interoperability, and inconsistent standards hinder effective safety assurance and regulatory adoption. The UK Department for Transport’s (DfT) “Safety Pool™ Scenario Database” project addresses these challenges by advancing the world’s largest open scenario repository. Building on WMG’s expertise, it enables trusted, transparent, and efficient scenario creation, enrichment, and validation for automated driving systems.

Objectives:

The Safety Pool™ Scenario Database project aims to enhance the UK’s capability in scenario-based testing and safety assurance for CCAM systems. The objectives are to:

1.Develop advanced toolchains for crowdsourced, incident-based, and continuous (24/7) scenario generation.

2.Enrich scenarios through realistic background traffic and Operational Design Domain (ODD) scenery integration.

3.Optimise user experience by enabling trajectory-based design, annotated storylines, and intelligent rating dashboards.

4.Create regulatory and user-defined use cases aligned with UNECE frameworks (e.g., R157 ALKS, R171 DCAS, LSAD).

5.Align scenario tagging with latest standards (ISO 34503, BSI PAS 1883:2025, BSI 1891) and develop ODD validation and search tools.

6.Strengthen interoperability and cybersecurity through improved APIs, cloud integration, and third-party audits.

The overall goal is to establish a comprehensive, standardised, and secure platform supporting scalable safety validation for automated driving technologies.

Approach and Innovation:

The project adopts a modular, AI-assisted, and standards-driven approach across six work packages, covering scenario generation, enrichment, optimisation, ODD alignment, and secure integration. It leverages the Safety Pool™ Studio as a collaborative interface for public and regulatory engagement, democratising scenario creation and validation. Novel toolchains—such as real-time incident reconstruction, complexity scoring, and trajectory conversion, enable automated, traceable, and interoperable workflows. By embedding ISO and ASAM standards (OpenSCENARIO, OpenLABEL, OpenDRIVE), the platform ensures cross-tool compatibility and regulatory readiness. Advanced ODD parsing, semantic tagging, and behaviour taxonomies enhance search precision and traceability. The integration of cybersecurity audits and API extensions further strengthens resilience and accessibility. Collectively, this approach delivers a scalable, user-centred infrastructure aligned with UK and international safety frameworks.

Impact and Next Steps:

The Safety Pool™ Scenario Database will reinforce the UK’s leadership in autonomous vehicle safety assurance, enabling a unified framework for data-driven validation and regulatory collaboration. It will accelerate certification readiness, reduce testing costs, and promote innovation by providing standardised, reusable, and high-fidelity scenarios. The project’s outcomes will feed into global standards (ISO, ASAM, UNECE) and support continuous learning through in-service monitoring. Long-term, the Safety Pool™ ecosystem will serve as the cornerstone of a trusted, interoperable, and sustainable validation infrastructure, empowering policymakers, industry, and researchers to advance CCAM safety and public confidence.

DORA: Developing Objective and quantifiable Risk Assessment for CAV (DORA)

• Funder: Innovate UK
• Partners: IDIADA AUTOMOTIVE TECHNOLOGY UK LTD

Context and Challenge:

There is an urgent need for objective, quantifiable measures to assess Autonomous Vehicle (AV) safety and performance, a critical step towards accelerating Connected and Automated Mobility (CAM) adoption in the UK. Current AV development and regulation lack clear methods to translate the subjective notion of a “careful and competent driver” into measurable criteria. DORA addresses this gap by aligning human driving standards with AV capabilities and introducing methodologies to quantify risk in complex, dynamic environments.

Objectives:

The DORA project aims to advance Connected and Autonomous Vehicle (CAV) validation and support the AV industry by delivering three key outcomes: a framework that quantifies AV driving behaviour, a robust testing methodology and recommendations for updating AV standards and regulations. Central to this is the creation of a novel framework that bridges human driving standards with AV capabilities, ensuring that the subjective notion of a “careful and competent driver” is translated into objective, measurable criteria. DORA will develop methodologies to quantify risk in complex, dynamic environments and establish clear stopping criteria for AV testing. These outputs will provide universally applicable benchmarks for AV validation and certification while informing future regulation and insurance models. By aligning with both established industry practices and emerging standards, DORA offers a holistic, evidence-based approach to safety assurance, supporting the safe and accelerated adoption of autonomous mobility in the UK.

Approach and Innovation:

The novel framework within DORA translates the concept of a "careful and competent driver" into quantifiable metrics for AVs. This involves analysing human driving data to extract key performance indicators, adapting these indicators for AV capabilities, and validating them within a robust simulation environment. The framework improves upon existing safety standards by providing specific, measurable criteria directly linked to human driving performance, addressing a major gap in current approaches. By applying established technologies such as data analytics and simulation to the new domain of AV risk assessment, while simultaneously developing novel methodologies for quantifying risk in complex, dynamic driving environments, the DORA approach ensures both innovation and practicality. The outputs directly address the need for clear, objective criteria in AV safety assessment and provide tangible tools for AV developers, regulators, and testing organizations to evaluate and improve AV performance. In doing so, DORA advances safety assurance and builds confidence for the safe deployment of autonomous mobility.

Impact and Next Steps:

DORA aligns with several Government priorities that include the UK's goal to be a world leader in CAM technologies that is championed through CCAV, the "Build Back Better" plan by fostering innovation and high-skilled job creation, helping to reduce regional economic disparities by distributing high-tech job opportunities. By strengthening AV safety assurance processes, DORA will accelerate AV adoption, improving mobility and independence for an estimated 1–2 million UK residents. Its outcomes will inform and shape future AV regulation, supporting the UK’s desire to be a leader in road safety initiatives and potentially reducing road accidents by 20–30% through improved AV standards.

CERTAIN

• Funding value: €1.08 million
• Partners: Applus IDIADA Automotive Technology SA, IDIADA UK Ltd, IDIADA Fahrzeugtechnik GmbH, Universitat Politècnica de Catalunya, BASt, CERTH, DAF Trucks N.V., Emotion3D, ERTICO – ITS Europe, I2CAT Foundation, ICCS, Infineon Technologies AG, Jaguar Land Rover Ltd, Karlsruhe Institute of Technology, University of Leeds, University of Warwick, TNO, Eindhoven University of Technology, Vicomtech, Valeo, ZF Friedrichshafen AG, SWOV, Technische Hochschule Ingolstadt, MAP Transport and Mobility, VAIVE Logistics SL, RWTH Aachen University, SIE-NL.

Project Context and Challenge:
Ensuring the safety, resilience, and societal acceptance of Connected, Cooperative and Automated Mobility (CCAM) systems is one of Europe’s most pressing transport challenges. Traditional validation methods—based on static testing and fixed certification cycles—cannot cope with the dynamic nature of AI-driven systems that evolve through software updates and learning processes. CERTAIN addresses this challenge by developing a continuous, human-centric safety assurance framework for all levels of automation, supporting the safe, trusted, and adaptive deployment of CCAM technologies.

Objectives:
CERTAIN aims to establish a resilient, continuous, and human-centred Safety Assurance Framework (SAF) for CCAM, extending existing methods developed under projects such as HEADSTART and SUNRISE. Its overarching goal is to embed safety, trust, acceptance, and comfort principles into the assurance process, ensuring that ADS and cooperative systems remain safe throughout their lifecycle. Specifically, the project will validate eleven representative sub-use cases spanning SAE Levels 2–4 and multiple environments—urban, highway, and logistics—by combining virtual, proving-ground, and real-world testing. The SAF will integrate trustworthy AI validation, human-machine interaction (HMI) assessment, and in-service monitoring to ensure continuous feedback and improvement. CERTAIN will also deliver harmonised procedures and tools aligned with the UNECE NATM pillars—Audit, Performance Assurance, and ISMR—and will provide guidelines and handbooks that support future regulatory frameworks, consumer assessment programmes, and standardisation efforts across the EU mobility ecosystem.

Approach and Innovation:
CERTAIN adopts a multi-pillar assurance approach consistent with the UNECE New Assessment/Test Method (NATM), building upon the methodological foundation established by SUNRISE. It extends the SAF into a lifecycle-oriented framework where assurance activities continue beyond type-approval through in-service monitoring. The approach integrates scenario-based verification, trustworthy AI evaluation, and user-centred safety metrics for HMI assessment. Technically, the project combines virtual simulation, hardware-in-the-loop, vehicle-in-the-loop, and on-road validation environments within a harmonised toolchain to demonstrate safety across diverse contexts. Innovation arises from linking these domains through continuous data exchange and credibility assessment of models and simulations, ensuring that evidence remains valid and traceable throughout system evolution. CERTAIN also pioneers V2X-based safety validation, lifecycle feedback loops, and trustworthy-AI auditability, enabling regulators and industry to transition from static certification to dynamic, data-driven assurance of CCAM safety.

Impact and Next Steps:
CERTAIN will reinforce Europe’s leadership in safe and trustworthy mobility by delivering a validated, human-centric SAF that bridges research, industry, and regulation. The project’s methods and data assets will shape future EU type-approval procedures, consumer testing (Euro NCAP), and international standardisation (UNECE, ISO, SAE, ASAM). Its long-term impact will include improved interoperability between assurance frameworks, reduced duplication of validation efforts, and enhanced societal acceptance of automation. Looking forward, CERTAIN will extend its framework towards in-service monitoring and digital certification, enabling continuous regulatory oversight and strengthening Europe’s Vision Zero ambitions.