A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

Riegler, Andreas; Riener, Andreas; Holzmann, Clemens · 2021 · OpenAlex-citations

DOI: 10.3389/fhumd.2021.689856

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Summary

This systematic review analyzes 12 years of research (2009–2020) on virtual reality (VR) applications in automated driving (AD). The study addresses the heterogeneous and non-transparent state of existing literature, aiming to classify findings by application area, automation level, and methodology. The motivation stems from the rapid advancement of AD systems and mixed reality technologies, which necessitates a consolidated understanding of how VR is utilized for user studies, interface design, and safety evaluation. The authors sought to identify research gaps and provide recommendations for future studies in the automotive human-computer interaction domain. The methodology involved a three-stage process: search, rating, and coding. The authors searched major scientific databases (ACM Digital Library, IEEE Xplore, Science Direct, Wiley Online Library, and Google Scholar) using keywords related to AR, VR, mixed reality, and automated driving. From an initial pool of 293 candidate papers, 176 core relevant papers were selected based on inclusion criteria requiring detailed user study descriptions. These papers were categorized into twelve application areas, including navigation, safety, trust, and external human-machine interfaces. The review analyzed study designs, devices used (e.g., head-mounted displays, CAVEs, monitors), and levels of vehicle automation (SAE Levels 0–5). The findings reveal a significant increase in VR-related publications, with 2020 seeing more than ten times the output of 2009. Most studies (76%) employed within-subjects designs and were conducted in controlled laboratory environments (83%), limiting external validity. The majority of research focused on lower automation levels (SAE L0–2), with fewer studies addressing higher levels (L3–5). Key application areas included UI design and prototyping, vulnerable road users, and safety. Common devices included head-mounted displays and CAVEs. The review highlights that while VR allows for safe, reproducible experiments, many studies lack pilot testing and standardized measures for simulator sickness and user experience. The significance of this work lies in its comprehensive classification of VR applications in AD, providing a roadmap for researchers. The authors identify open challenges, such as the need for standardized methodologies for self-report, behavioral, and physiological measures. They recommend future research focus on higher automation levels, naturalistic field studies, and the development of external human-machine interfaces for vehicle-pedestrian communication. By mapping the current status quo, the review aims to help the automotive UI community improve usability and user experience in VR-based AD research.

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Topics

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• hud ar windshield
• acceptance adoption
• automation
• ehmi external hmi
• situational awareness
• simulator validity fidelity

Information type

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• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model