Get ready for automated driving using Virtual Reality. (September 2018)

• Record Type: Journal Article
• Authors: Sportillo, Daniele; Paljic, Alexis; Ojeda, Luciano
• Is Part Of: Accident analysis and prevention. Volume 118(2018)
• Page Start: 102
• Page End: 113
• Publication Date: 2018-09
• Subjects: Conditionally automated vehicles -- Virtual Reality -- Head-Mounted Display -- Take-over request -- Training; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.06.003
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Drivers of Level-3 automated vehicles must be trained before their first ride. Traditional hand-over does not ensure the acquisition of operational skills. Virtual Reality systems allow for safely familiarizing drivers with the vehicle. A Head-Mounted Display, a fixed-base simulator and a user manual were evaluated. The HMD provided promising results and was the preferred training system. 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. Interacting with the car in the proper way from the first ride is crucial for car and road safety in general. For this reason, it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems. In this context, Virtual Reality (VR) 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 addition, Head-Mounted Display (HMD)-based VR (light VR) would allow for the easy deployment of such training systems in driving schools or car dealerships. In this study, the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated. The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures. Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator. Results show that the training system affects the take-over performances. Moreover, self-reported measures indicate that the light VR training is preferred with respect to the other systems. Finally, another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.