AR4CAD: Creation and Exploration of a Taxonomy of Augmented Reality Visualization for Connected Automated Driving

Abstract

Infrastructure-mounted sensors that monitor roads can provide essential information for manual drivers and automated vehicles, e.g., positions of other vehicles occluded by buildings. However, human drivers and passengers have to trust and accept their use. This raises the question of how trust can be increased in such a scenario. One important factor for this is understanding the available information, including its quality and, for passengers of automated vehicles, the actions planned based on it. For this, augmented reality is a promising visualization technology because it can present the relevant information integrated into the physical world. Thus, this work develops a taxonomy of augmented reality visualizations for connected automated and manual driving. It is intended to classify and compare existing visualizations, identify novel visualizations, and provide a common language for discussions. The use case infrastructure-supported automated driving is explored by suggesting augmented reality visualizations to inform passengers of automated vehicles and are intended to increase trust. They present information available from infrastructure and onboard sensors as well as the driving decisions based on it. Finally, we evaluated the visualizations’ influence on trust in an automated vehicle by conducting a driving simulator study (N=18). Results indicate a high dependency of trust on presenting driving decisions and information on road users but less on location-specific information.