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RESEARCH OBJECTIVE Safety assurance of Cooperative, Connected, and Automated Mobility (CCAM) systems, is a crucial factor for their successful adoption and deployment in society. This requires a strong safety argumentation, which remains a significant challenge worldwide. Although automotive stakeholders seem to agree on a scenario-based approach, the current situation tends to silo solutions which hampers the large-scale and safe introduction of CCAM systems. For these reasons, the SUNRISE project has been initiated (under the EU’s funding programme for research and innovation), with the objective to develop a scalable CCAM Safety Assurance Framework, interconnecting silos and making them collaborate in a harmonized way. METHODOLOGY CCAM safety assurance requires a strong safety argumentation. For higher levels of automation, case-by-case validation testing is not feasible due to the high number of scenarios involved. Public road validation is another option, but since conditions are uncontrollable, this option is ineffective and therefore expensive. Virtual simulation allows for cost effective testing of large scenario volumes, although the reliability of the results is often compromised. For these reasons, a mixture of physical and virtual testing has emerged as a promising approach. And to overcome the tendency towards silo solutions, SUNRISE results will be based on standardization and harmonization. PLANS The following figure shows the work plan applied in the SUNRISE project. FIGURE (see PDF) A central role can be observed for the Safety Assurance Framework. This framework is based on several methods and a toolchain. For example, a method for scenario selection and allocation, and tools for physical and virtual evaluation. Another important component, is the data framework with its structure and governance mechanisms, as well as an ontology, laying the foundation for querying and interoperability. The Safety Assurance Framework will be put to the test by a series of relevant and realistic test cases. The development of the method, toolchain, and data framework, is based on existing safety assurance frameworks and related initiatives. Technical results include a CCAM safety assessment method and toolchain, as well as an interactive user handbook, and an EU data framework including a test case library and a scenario database (with a limited number of scenarios). LIMITATIONS Whereas predecessor project HEADSTART worked on a technology readiness level (TRL) of 3 to 4, under SUNRISE the TRL will be raised to 6 or 7. Although the technology becomes more mature under SUNRISE, additional efforts will still be required before users can fully apply and exploit the Safety Assurance Framework created under SUNRISE. Furthermore, although SUNRISE will develop a scenario database, this database will contain a very limited number of scenarios. However, this database will be scalable, allowing for potentially being filled with a much larger number of scenarios in the future. WHAT’S NEW? The SUNRISE project does not start from scratch. It is based on predecessor project HEADSTART, which finished early 2022. Based on the methodologies and harmonization efforts performed in HEADSTART, SUNRISE will work towards the establishment of a broader CCAM Safety Assurance Framework. With respect to HEADSTART, the number of use cases will be significantly increased, as well as the emphasis on virtual simulation. New items include a European scenario database, and a proposal for a type approval scheme. CONCLUSIONS At the time of writing this abstract, the SUNRISE project only just started. It is therefore too early to deep dive into technical matters, or to draw technical conclusions. First technical results or conclusions might be included in the paper, or the final presentation at the event.



Mr. Stefan de Vries, Project Manager - Connected and Automated Vehicles, Applus+ IDIADA

SUNRISE: Safety Assurance Framework for Connected and Automated Mobility Systems

FWC2023-SCA-023 • FISITA World Congress 2023 • Integrated safety, connected & automated driving

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