[Paper Review] Familiarization tours for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

Mahdi Ebnali, Richard Lamb+1 more|arXiv (Cornell University)|Feb 19, 2020

Human-Automation Interaction and Safety65 references4 citations

TL;DR

This study evaluates virtual reality (VR) familiarization tours for first-time users of highly automated vehicles, comparing low- and high-fidelity VR with video and control conditions. High-fidelity VR significantly improved takeover quality and trust, with all groups converging after three exposures, demonstrating that immersive, interactive training reduces reliance on initial trust and enhances learning through experience.

ABSTRACT

Research in aviation and driving has highlighted the importance of training as an effective approach to reduce the costs associated with the supervisory role of the human in automated systems. However, only a few studies have investigated the effect of pre-trip familiarization tours on highly automated driving. In the present study, a driving simulator experiment compared the effectiveness of four familiarization groups, control, video, low fidelity virtual reality (VR), and high fidelity VR on automation trust and driving performance in several critical and non-critical transition tasks. The results revealed the positive impact of familiarization tours on trust, takeover, and handback performance at the first time of measurement. Takeover quality only improved when practice was presented in high-fidelity VR. After three times of exposure to transition requests, trust and transition performance of all groups converged to those of the high fidelity VR group, demonstrating that: a) experiencing automation failures during the training may reduce costs associated with first failures in highly automated driving; b) the VR tour with high level of interaction fidelity is superior to other types of familiarization tour, and c) uneducated and less-educated drivers learn about automation by experiencing it. Knowledge resulting from this research could help develop cost-effective familiarization tours for highly automated vehicles in dealerships and car rental centers.

Motivation & Objective

• To investigate how different levels of interaction fidelity in virtual reality familiarization tours affect trust and performance in highly automated driving.
• To assess whether pre-trip familiarization reduces the cognitive and performance costs associated with human supervision in automated vehicles.
• To determine whether exposure to automation failures during training improves long-term performance and trust in automated systems.
• To explore whether uneducated and less-educated drivers benefit from experiential learning in high-fidelity VR environments.

Proposed method

• Conducted a driving simulator experiment with 120 first-time users of highly automated vehicles.
• Assigned participants to four familiarization conditions: control (no tour), video tour, low-fidelity VR, and high-fidelity VR.
• Measured trust, takeover quality, and handover performance across multiple critical and non-critical transition tasks.
• Used standardized metrics to assess trust (post-task surveys) and performance (latency, steering accuracy, time to respond).
• Repeated exposure to transition requests across three sessions to assess learning over time.
• Employed a within-subjects design to compare performance changes across conditions after repeated exposure.

Experimental results

Research questions

• RQ1 Does high-fidelity VR familiarization lead to better trust and takeover performance than low-fidelity VR or video tours in first-time users of automated vehicles?
• RQ2 How does repeated exposure to transition requests affect trust and performance across different familiarization conditions?
• RQ3 To what extent do automation failures during training reduce the negative impact of first-time failures in real automated driving scenarios?
• RQ4 Can uneducated or less-educated drivers achieve comparable performance gains through immersive VR training as more educated users?

Key findings

• High-fidelity VR familiarization significantly improved takeover quality compared to video, low-fidelity VR, and control groups at first measurement.
• After three exposures to transition requests, trust and transition performance converged across all groups, indicating that repeated exposure mitigates initial differences.
• Experiencing automation failures during training reduced the performance costs associated with first-time failures in automated driving.
• Uneducated and less-educated drivers showed measurable learning gains through high-fidelity VR, suggesting that immersive interaction supports learning regardless of education level.
• The high-fidelity VR condition was superior in enhancing trust and performance, especially in critical takeover scenarios.
• The study demonstrates that high-fidelity VR familiarization is a cost-effective method for improving user readiness for highly automated vehicles in real-world settings like dealerships and rental centers.