The Impact of System Transparency on Passenger’s Quality of Experience in Highly Automated Driving

Automated driving is transforming the nature of passenger interaction and user experience. In highly automated vehicles, establishing passengers' trust is essential for interactions and key to achieving a positive Quality of Experience (QoE). Improving system transparency is widely employed among the various methods for calibrating trust. However, increasing transparency can also harm QoE by increasing passenger workload. This research examines this trade-off through an experiment conducted in a driving simulation environment. Our findings reveal a positive correlation between system transparency and trust but a non-monotonic correlation with the workload. This suggests that there may be better options than maximizing system transparency for QoE design, and workload management should be considered to optimize system transparency. Based on our design practice and verification, we provide suggestions for improving system transparency, including information integration, synergy, feedback, and auxiliary prediction.