🤖 AI Summary
This work addresses the integration bottleneck in automotive software development caused by reliance on costly hardware-in-the-loop (HiL) testing when physical hardware is unavailable. The authors propose a virtual testing and integration environment that leverages intelligent agents to automatically generate instruction-accurate SystemC/TLM 2.0 processor models, enabling execution of real software binaries prior to the availability of physical ECUs. A novel closed-loop modeling mechanism, driven by GDB feedback and enhanced through automated differential testing and iterative refinement, significantly improves the behavioral fidelity of virtual ECUs and facilitates shift-left testing. Prototype validation demonstrates that critical CPU behaviors remain within acceptable risk bounds, supporting reproducible testing, fault injection, and verification aligned with functional safety standards, thereby offering a viable pathway toward high-fidelity virtual ECU digital twins.
📝 Abstract
Automotive software increasingly outpaces hardware availability, forcing late integration and expensive hardware-in-the-loop (HiL) bottlenecks. The InnoRegioChallenge project investigated whether a virtual test and integration environment can reproduce electronic control unit (ECU) behavior early enough to run real software binaries before physical hardware exists. We report a prototype that generates instruction-accurate processor models in SystemC/TLM~2.0 using an agentic, feedback-driven workflow coupled to a reference simulator via the GNU Debugger (GDB). The results indicate that the most critical technical risk -- CPU behavioral fidelity -- can be reduced through automated differential testing and iterative model correction. We summarize the architecture, the agentic modeling loop, and project outcomes, and we extrapolate plausible technical details consistent with the reported qualitative findings. While cloud-scale deployment and full toolchain integration remain future work, the prototype demonstrates a viable shift-left path for virtual ECU twins, enabling reproducible tests, non-intrusive tracing, and fault-injection campaigns aligned with safety standards.