Discount Driver Mental Workload Assessment
Abstract
Increasingly, drivers have access to many in-vehicle applications and notifications can create interruptions during critical moments. In order to provide safe notifications to drivers, we need a way of knowing when the complexity of the driving task, or associated mental workload, is not too high. In this paper we first report on a pilot study that demonstrated the impact of mental workload on willingness to receive notifications in different driving tasks. We then report on a study where a novel method for assessing how different driving situations affect driver mental workload is developed and evaluated. We developed the “discount mental workload assessment” method, by iteratively designing simulation videos of different driving situations, along with an online questionnaire that assessed driver workload. We also identified features of driving situations that contribute to higher mental workload, as a first step towards predicting when notifications to the driver can be safely made. We found that participants were in general agreement about the level of driving mental workload associated with the different driving scenarios. We propose that the discount mental workload assessment method developed here may also be useful in other HCI (non-driving) contexts.
Authors: Haoyan Jiang, Sachi Mizobuchi, Mark Chignell
Published in: IEEE International Conference on Human-Machine Systems (ICHMS) (2024)