Req2Road: A GenAI Pipeline for SDV Test Artifact Generation and On-Vehicle Execution

TL;DR

This paper is a feasibility and architectural demonstration of an end-to-end requirements-to-test pipeline for SDV subsystems, evaluated on a CPDS case in simulation and Vehicle-in-the-Loop settings.

Abstract

Testing functionality in Software-Defined Vehicles is challenging because requirements are written in natural language, specifications combine text, tables, and diagrams, while test assets are scattered across heterogeneous toolchains. Large Language Models and Vision-Language Models are used to extract signals and behavioral logic to automatically generate Gherkin scenarios, which are then converted into runnable test scripts. The Vehicle Signal Specification (VSS) integration standardizes signal references, supporting portability across subsystems and test benches. The pipeline uses retrieval-augmented generation to preselect candidate VSS signals before mapping. We evaluate the approach on the safety-relevant Child Presence Detection System, executing the generated tests in a virtual environment and on an actual vehicle. Our evaluation covers Gherkin validity, VSS mapping quality, and end-to-end executability. Results show that 32 of 36 requirements (89\%) can be transformed into executable scenarios in our setting, while human review and targeted substitutions remain necessary. This paper is a feasibility and architectural demonstration of an end-to-end requirements-to-test pipeline for SDV subsystems, evaluated on a CPDS case in simulation and Vehicle-in-the-Loop settings.

Figures (5)

• Figure 1: Overview of our proposed GenAI-based test generation.
• Figure 2: Req2Road workflow: requirements to executable test artifacts.
• Figure 3: CPDS sensors and actuators considered in this study.
• Figure 4: CPDS escalation logic with time-limited escalation stages.
• Figure 5: ViL setup: child seat with instrumented surrogate occupant (left) and corresponding HVAC state in the infotainment display (right).