Digital Twins for Early Verification and Validation of Autonomous Driving Features: Open-source Tools and Standard Formats
Paper in proceeding, 2024

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.

Author

Beatriz Cabrero-Daniel

University of Gothenburg

Software Engineering 2

Ahmed Yasser Abdelkarim

University of Gothenburg

Axel Broberg

University of Gothenburg

IEEE Intelligent Vehicles Symposium, Proceedings

19310587 (ISSN) 26427214 (eISSN)

2477-2482
9798350348811 (ISBN)

35th IEEE Intelligent Vehicles Symposium, IV 2024
Jeju Island, South Korea,

Enabling Virtual Validation and Verification for ADAS and AD Features

VINNOVA (2021-05043), 2022-04-01 -- 2024-06-30.

Subject Categories

Computer Science

DOI

10.1109/IV55156.2024.10588808

More information

Latest update

8/5/2024 8