The proposed actions are expected to address all of the following aspects:

1. AI based tools to transform raw traffic data into reliable, plausibility-proofed data as well as tools for automatic scenario identification and extraction from that data, including the detection of edge cases – the relatively rare, but particularly challenging traffic situations.
2. Generation of variations of scenarios (starting from those based on real traffic data and creating synthetic entries to the scenario database) with a focus on extending ODDs (including adverse weather conditions).
3. Integration of the above in an automatic processing chain with standardised, open interfaces to enable the efficient and seamless use of data from different sources.
4. Ensuring reliable merging of scenarios from different data sources (different projects, different vehicles and stationary units, different perspectives etc.).
5. Feeding the resulting scenarios in an openly accessible dynamic scenario database, which can be used for the development, training, virtual testing and type approval validation of CCAM systems, and which should be connected to or integrate existing national databases as far as possible.
6. Defining approaches and methods to handle uncertainty and the possibility of errors that might propagate in the assessment, including algorithms for their quantification.
7. Demonstration, assessment of the potential and upscaling of (quasi-)stationary sensor units to record high quality big traffic data in various environments, as well as under various environmental conditions and to identify relevant scenarios making use of the processing chain. When recording traffic data in urban areas, proposed action should aim at:
   • high geographic coverage,
   • high seasonal coverage including adverse environmental conditions (e.g. extreme weather conditions) and their synchronized recording and
   • coverage of complex traffic environments including the interaction with other road users (e.g. pedestrians, bicyclists, users of personal mobility devices).
8. Evaluating different approaches to identify relevant scenarios on rural roads based on the developed processing chain and on traffic data to be recorded on various types of rural roads.
9. Exploring the potential of complementing scenarios extracted from real traffic data with scenarios extracted from validated, highly detailed traffic simulations, including the use of AI to generate edge cases and other adversarial driving conditions in such simulations.
10. Development of a mechanism for the continuous generation of updates of the dynamic scenario database, including an arrangement for the organisational set-up, governance and financial management of the required activities and resources.

The research will require due consideration of cyber security and both personal and non-personal data protection issues, including GDPR.