Light Virtual Reality systems for the training of conditionally automated vehicle drivers
This addresses the need for effective training methods for drivers in automated vehicles, though it is incremental as it builds on existing VR training approaches.
The study tackled the problem of training drivers to respond to take-over requests in conditionally automated vehicles by comparing a Head-Mounted Display (HMD)-based virtual reality training program to a user manual and a fixed-base simulator, finding that the HMD-based training improved take-over performances and was preferred by users.
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.