Digital Twins for Early Verification and Validation of Autonomous Driving Features: Open-source Tools and Standard Formats

Getting real data from safety critical systems for specific, rare situations (e.g., edge cases) is challenging. Moreover, data gathered during operations (e.g., crash reports) are not often publicly accessible and reports might be incomplete. However, covering all scenarios is very important for Verification and Validation (V&V) of safety-critical systems such as AVs. Therefore, synthetic data could be used for V&V to fill that gap. Synthetic data generation, labelling, and validation are open challenges, though. To the best of the authors’ knowledge, no standard methods for integrating synthetic data into V&V are shared across automotive-domain companies. Therefore, this study (i) gathers expert knowledge on current practices for Digital Twins for V&V development, (ii) proposes a general 6-stage pipeline for synthetic data usage within an early V&V process, and (iii) discusses open source tools and formats standardisation of synthetic data use within V&V. The open-source tools and format standardisation may facilitate the integration of synthetic data into the V&V process. The proposed pipeline and mapping study, provide a foundation for future research on synthetic data use within V&V.