Advancing Connected Vehicle Capabilities with Scalable and Flexible Anything-In-The-Loop Testing Framework

Connected vehicle technologies have the potential to improve safety and energy efficiency. However, an efficient and robust testing framework that bridges simulations and realworld conditions is required to accelerate their validation and deployment. This paper demonstrates a streamlined approach to validating and testing connected vehicles through simulations, MicroAutobox-in-the-loop (MABx-IL) tests, MABx-vehicle-in-the-loop (MABx-VIL), and Human-MABx-VIL tests. The demonstration leverages our anything-in-the-loop (XIL) framework, which interfaces various components (e.g., simulation models, MABx, vehicle, human drivers) to evaluate connected vehicle performance in a simulated Chicago urban corridor. Our automatic model-building process has validated its flexibility, scalability, and consistency, enabling stage-by-stage progressive integration of physical components in XIL testing. Using target electric vehicle (Hyundai Ioniq 5), the Human-MABx-VIL study validated eco-speed advisory driving through a phone-based humanmachine interface (HMI), with drivers successfully performing eco-approaches to virtual signalized intersections along the road. This effort was preceded by extensive simulations, MABx-IL, and MABx-VIL, progressively swapping virtual components within the XIL framework. It underscores the necessity of streamlined validation and testing and highlights the potential of connected eco-speed advisory driving using real-time vehicle-to-everything (V2X) information.

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Publication Details

Published
2025-06-18
DOI
https://doi.org/10.1109/itec63604.2025.11097967
Primary Topic
Real-Time Systems Scheduling
Type
article
Field-Weighted Citation Impact
0.00

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article

Advancing Connected Vehicle Capabilities with Scalable and Flexible Anything-In-The-Loop Testing Framework

Michael Pamminger, Kevin Stutenberg, Qian Peng, Priyash Misra et al.
Real-Time Systems Scheduling
article

Advancing Connected Vehicle Capabilities with Scalable and Flexible Anything-In-The-Loop Testing Framework

Michael Pamminger, Kevin Stutenberg, Qian Peng, Priyash Misra, Dominik Karbowski, Miriam Di Russo, Debashis Das, Jihun Han, Trevor Crain, Jongryeol Jeong
article en

Abstract

Connected vehicle technologies have the potential to improve safety and energy efficiency. However, an efficient and robust testing framework that bridges simulations and realworld conditions is required to accelerate their validation and deployment. This paper demonstrates a streamlined approach to validating and testing connected vehicles through simulations, MicroAutobox-in-the-loop (MABx-IL) tests, MABx-vehicle-in-the-loop (MABx-VIL), and Human-MABx-VIL tests. The demonstration leverages our anything-in-the-loop (XIL) framework, which interfaces various components (e.g., simulation models, MABx, vehicle, human drivers) to evaluate connected vehicle performance in a simulated Chicago urban corridor. Our automatic model-building process has validated its flexibility, scalability, and consistency, enabling stage-by-stage progressive integration of physical components in XIL testing. Using target electric vehicle (Hyundai Ioniq 5), the Human-MABx-VIL study validated eco-speed advisory driving through a phone-based humanmachine interface (HMI), with drivers successfully performing eco-approaches to virtual signalized intersections along the road. This effort was preceded by extensive simulations, MABx-IL, and MABx-VIL, progressively swapping virtual components within the XIL framework. It underscores the necessity of streamlined validation and testing and highlights the potential of connected eco-speed advisory driving using real-time vehicle-to-everything (V2X) information.

Argonne National Laboratory (US)
U.S. Department of Energy
Openalex Percentile: Top 80%
Real-Time Systems Scheduling
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Advancing Connected Vehicle Capabilities with Scalable and Flexible Anything-In-The-Loop Testing Framework — Michael Pamminger, Kevin Stutenberg, et al. · (2025) | TGRS Research Map | TGRS