arXiv:2603.05279cs.ROcs.SY2026-03中稿 · publication at the…

用虚实联动技术测试自动驾驶中央服务器,更真实高效。

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

  • 构建车-数字孪生体联动系统,实现软硬件一体化测试
  • 直接在实车硬件运行完整算法,避免中间层和刷写
  • 适合自动驾驶系统早期验证,降低开发与集成成本

仿真在汽车软件开发阶段至关重要,但纯虚拟仿真因建模限制难以完全反映真实世界因素。为此,本文提出一种基于车辆在环(ViL)与数字孪生技术的集中式电子电气架构测试框架,将实车测试台架上的物理车辆与其同步的虚拟副本连接,形成虚实联动系统。该方法为基于集中式架构的自动驾驶算法提供安全、可复现、真实且低成本的验证平台,无需分别测试单个物理电控单元及其通信协议。相比传统方法,本框架在仿真后直接在实车硬件上运行完整的自动驾驶软件,省去烧录与中间层,实现虚拟与物理系统的无缝融合,精准反映集中式电子电气架构的行为。同时,混合使用模拟与物理环境测试,减少早期开发阶段对全硬件集成的依赖。实验案例表明,该框架在多种测试场景中均具有效性,有望显著降低未来自动驾驶系统测试的开发与集成工作量。

原文摘要 · Abstract (English)

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.

自动驾驶数字孪生测试框架集中式架构

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