From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving

• URL: http://arxiv.org/abs/2603.05279v1
• Date: Thu, 05 Mar 2026 15:23:28 GMT
• Title: From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving
• Abstract summary: This work presents a test framework for automotive software on the centralized E/E architecture.<n>The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment.
• Score: 12.14436913195805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation is one of the most essential parts in the development stage of automotive software. However, purely virtual simulations often struggle to accurately capture all real-world factors due to limitations in modeling. To address this challenge, this work presents a test framework for automotive software on the centralized E/E architecture, which is a central car server in our case, based on Vehicle-in-the-Loop (ViL) and digital twin technology. The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment. Our approach provides a safe, reproducible, realistic, and cost-effective platform for validating autonomous driving algorithms with a centralized architecture. This test method eliminates the need to test individual physical ECUs and their communication protocols separately. In contrast to traditional ViL methods, the proposed framework runs the full autonomous driving software directly on the vehicle hardware after the simulation process, eliminating flashing and intermediate layers while enabling seamless virtual-physical integration and accurately reflecting centralized E/E behavior. In addition, incorporating mixed testing in both simulated and physical environments reduces the need for full hardware integration during the early stages of automotive development. Experimental case studies demonstrate the effectiveness of the framework in different test scenarios. These findings highlight the potential to reduce development and integration efforts for testing autonomous driving pipelines in the future.

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