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.
Figures & tables
Fig. 0 : Overview of the proposed ViL validation framework with Central Car Server (CeCaS)-Computer as an alternative to the in-vehicle central car server. The virtual vehicle in the simulation environment serves as the digital twin of the test vehicle on the test bench.
Name
Simulation Computer
Central Car Computer
CPU
AMD TRP 5995WX
AMD TRP 5955WX
64 Cores at 2.7 GHz
16 Cores at 2.7 GHz
GPU
6 x RTX 4090 24 GB VRAM
RTX 4090 24 GB VRAM
RAM
256 GB DDR4-3200 RAM
64 GB DDR4-3200 RAM
TABLE I : Hardware setups - The simulation computer is equipped with six graphics processors to handle the high computational load of simulating numerous sensors, whereas the vehicle’s central computer provides significantly less computing power in order to emulate a realistic operational scenario.
Fig. 1 : Mounting points of the front (left) and rear (right) axle to the test bench with adapters.
Fig. 2 : View from the cockpit of the test vehicle under manual-drive scenarios.
Fig. 3 : Vehicle front camera in the ACC scenario.
Fig. 4 : Left: Front camera view on the test vehicle with the person detected. Right: Virtual simulation scenario and digital twin of the person spawned on the track when the object is detected with the real camera.
Fig. 5 : Distance between two vehicles when the ego-vehicle approaches.
Fig. 6 : Connection latency with FPS of the camera = 2 (left) and 5 (right)
Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based testing offers scalability, existing approaches often lack seamless integration with real vehicles and field data, limiting their fidelity in capturing dynamic, real-world interactions. To bridge this gap, this paper proposes a novel real-time hybrid digital twin platform. Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link. A custom-developed middleware serves as the critical bridge, synchronizing a real CAV's kinematic state as a shadow vehicle in the simulation and translating virtual control commands into chassis-actuating Controller Area Network (CAN) messages for closed-loop control. Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation. Experimental results demonstrate stable synchronization and effective closed-loop control with low latency, confirming the platform's practicality for multi-scenario CAV verification.
Kanglong Quan, Zhebing Xia, Linfeng Jiang +4
School of Advanced Technology Xi’an Jiaotong-Liverpool University, Jiangsu, China
We propose a mixed-reality, hardware-in-the-loop (HIL) testbed for autonomous vehicles that seamlessly integrates a physical testbed of mobile robots with a high-fidelity simulation environment. The virtual simulation enables the creation of diverse, safety-critical driving scenarios to validate state-of-the-art perception, planning, and control algorithms, while augmenting simulations with physical robots equipped with multimodal sensors in photorealistic virtual environments further facilitating rigorous validation. Our testbed also features vehicular connectivity using wireless communication and can accommodate a large number of agents through the combination of physical robots and virtual simulated agents, supporting research on multi-agent systems including Connected and Autonomous Vehicles (CAVs). Finally, we present a safety-guaranteed framework combining perception, planning and a novel online learning-based controller using Control Barrier Functions (CBFs) for CAVs. Experiments using the proposed framework are used to validate and demonstrate the key functionalities and the overall utility of the testbed to bridge the gap between simulation and real-world hardware deployment.
H. M. Sabbir Ahmad, Ehsan Sabouni, Emrullah Celik +4
Division of Systems Engineering, Boston University · Department of Electrical and Computer Engineering, Boston University
Autonomous driving systems require reliable safety validation before real-world deployment. However, large-scale road testing is costly, difffcult to reproduce, and inefffcient for exposing rare safety-critical scenarios. Conventional simulation improves repeatability, but an offfine simulator alone cannot continuously connect physical trafffc states, virtual reconstruction, algorithm evaluation, and scenario evolution. This paper proposes a risk-ffeld enhanced closed-loop digital twin framework for autonomous driving safety validation. The framework integrates physical data acquisition, data synchronization, virtual twin reconstruction, risk-aware scenario generation, autonomous driving algorithm evaluation, and safety analysis. A driving risk ffeld is introduced as a uniffed intermediate representation to describe obstacle, lane-departure, road-boundary, time-to-collision, and comfort-related risks around the ego vehicle. The risk ffeld ranks high-risk scenarios in the digital twin scenario library and provides dense safety guidance for reinforcement learning-based driving policies. A simulation-style evaluation protocol is designed to compare conventional reinforcement learning baselines, risk-penalty baselines, and the proposed risk-ffeld guided method. The study indicates that embedding explicit risk structure into digital twins can make autonomous driving validation more targeted, interpretable, and reusable, while its practical effectiveness remains bounded by model ffdelity, risk calibration, and sim-to-real transfer.
Yongzhi Liu
School of Mechanical Engineering Southeast University Nanjing, China