A Framework of Vehicle-Human Communication Features at Traffic Intersections to Enhance Trust and Situation Awareness
Abstract
Vehicle manufacturers are advancing their automated driving system (ADS) capabilities with enhanced transparency features. Research supports driving assistants (DA) and augmented reality (AR) displays for conveying the ADS status, actions, and road environment elements. However, providing continuous or irrelevant information degrades driving performance and attitudes towards the ADS. Therefore, the current study sought to create a framework for specific communication features that would enhance drivers’ trust and situation awareness via DA and AR stimuli. Participants watched various driving scenarios and provided their desired communication features to improve trust and situation awareness across modalities. Results identified key themes consistent across events (i.e., current/intended vehicle actions) as well as context-dependent themes such as police presence or pedestrian detection and location. In contrast, auditory cues were identified as redundant across events. These findings can support researchers to focus on relevant information to enhance drivers’ attitudes, awareness, and safety while operating ADS-equipped vehicles.
