Light Virtual Reality systems for the training of conditionally automated vehicle drivers

TL;DR

This study evaluates the effectiveness of a head-mounted display virtual reality training system for drivers of conditionally automated vehicles, showing improved take-over performance and user preference over traditional methods.

Contribution

Introduces an HMD-based VR training system for automated vehicle drivers and demonstrates its effectiveness compared to manuals and fixed-base simulators.

Findings

HMD-based VR training improves take-over performance.

Users prefer HMD-based VR training over other methods.

Training impacts driver response in simulated take-over scenarios.

Abstract

In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a take-over request when the system reaches its functional boundaries. In this context, Virtual Reality systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In this study, the effectiveness of an Head-Mounted display (HMD)-based training program for acquiring interaction skills in automated cars was compared to a user manual and a fixed-base simulator. Results show that the training system affects the take-over performances evaluated in a test drive in a high-end driving simulator. Moreover, self-reported measures indicate that the HMD-based training is preferred with respect to the other systems.