Software-Defined Vehicles (SDVs) are revolutionizing automotive development by decoupling hardware from software, enabling agile, continuous integration and deployment (CI/CD) workflows. This shift allows for early and frequent testing of vehicle functions—especially in Software-in-the-Loop (SiL) environments—using the same containerized applications that run in actual vehicles.

In our article “Software-defined Vehicle Accelerates Virtual Testing of ADAS-AD Functions”, recently published in ATZelectronic magazine issue 7-8/2025, we present a testing framework that integrates simulation environments with SDV architectures. Key features include:

• Scenario-based testing triggered by each functional increment.
• Machine-readable test cases and scenarios derived from model-based requirements and AI-generated inputs.
• Automated pipelines that build, deploy, and test applications across platforms (SiL, HiL, vehicle).
• Vehicle Abstraction Layer (VAL) using ROS2 and ASAM OSI standards to ensure compatibility and data translation between simulation and SDV apps.
• Centralized reporting and visualization tools for efficient result evaluation.

This approach significantly reduces development risks and accelerates feedback cycles, making it a powerful enabler for modern ADAS/AD function development.

📄 For full details, refer to the full Article: Software-defined Vehicle Accelerates Virtual Testing of ADAS-AD Functions | springerprofessional.de

Authors:
B.Eng, Thorsten Decker, Senior Software Engineer ADAS/AD
Dipl. Ing., Jörg Kottig, Team Leader Software Applications
M.Sc., Max-Arno Meyer, Senior Systems Consultant

References:
[1] Christiaens, S., Sontheim, F. & Börner, E. Modellbasiertes Systems Engineering beschleunigt Homologation von Softwareupdates. ATZ Elektron 19, 16–21 (2024). https://doi.org/10.1007/s35658-023-1564-3
[2] Temmen, T. et al. (2024). Application of Model-Based Systems Engineering Methods in Virtual Homologation Procedures for Automated Driving Functions. In: Heintzel, A. (eds) Automatisiertes Fahren 2024. AUDR 2024. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-45196-7_1
[3] Prathyusha Nama, Purushotham Reddy, & Guru Prasad Selvarajan. (2023). Leveraging Generative AI for Automated Test Case Generation: A Framework for Enhanced Coverage and Defect Detection. Well Testing Journal, 32(2), 74–91. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/110
[4] ASAM e.V.: ASAM OpenTestSpecification – Concept Paper. https://www.asam.net/standards/asam-test-specification (2024).
[5] Meyer, MA., Zouari, M., Bannenberg, S. et al. Machine-readable specification and intelligent cloud-based execution of logical test cases for automated driving functions. Autom Softw Eng 32, 10 (2025). https://doi.org/10.1007/s10515-024-00481-6
[6] ASAM e.V.: ASAM Open Simulation Interface (OSI). https://www.asam.net/standards/detail/osi/ (2024).
[7] Hidrive.eu, Projekthomepage https:// www.hi-drive.eu