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Toward an Automated Scenario-Based X-in-the-Loop Testing Framework for Connected and Automated Vehicles

Journal Article
12-05-04-0030
ISSN: 2574-0741, e-ISSN: 2574-075X
Published June 27, 2022 by SAE International in United States
Toward an Automated Scenario-Based X-in-the-Loop Testing Framework
                    for Connected and Automated Vehicles
Sector:
Citation: Kyriakopoulos, I., Jaworski, P., and Edwards, T., "Toward an Automated Scenario-Based X-in-the-Loop Testing Framework for Connected and Automated Vehicles," SAE Intl. J CAV 5(4):381-391, 2022, https://doi.org/10.4271/12-05-04-0030.
Language: English

Abstract:

Emerging technologies for connected and automated vehicles (CAVs) are rapidly advancing, and there is an incremental adoption of partial automation systems in existing vehicles. Nevertheless, there are still significant barriers before fully or highly automated vehicles can enter mass production and appear on public roads. These are not only associated with the need to ensure their safe and efficient operation but also with cost and delivery time constraints. A key challenge lies in the testing and validation (T&V) requirements of CAVs, which are expected to be significantly higher than those of traditional and partially automated vehicles. Promising methodologies that can be used toward this goal are scenario-based (SBT) and X-in-the-Loop (XiL) testing. At the same time, complex techniques such as co-simulation and mixed-reality simulation could also provide significant benefits. Nevertheless, the benefits of individual solutions are likely to be significantly smaller, if considered in isolation without any supporting test automation methods. This article attempts to combine existing knowledge and state of the art to explore the development of a framework for automating the T&V needs of CAVs. To this end, the integration of the VeriCAV framework for automating SBT with the Digital CAV Proving Ground Feasibility Study (DigiCAV) XiL mixed-reality CAV development and evaluation platform has been explored. The goal of the new framework is to enable an iterative and incremental approach across all stages of CAV development through the combination of optimal scenario generation and a comprehensive XiL scenario execution environment. This article presents an overview of the new framework as well as preliminary proof of concept results.