Identifying interaction types and functionality for automated vehicle virtual assistants: an exploratory study using speech acts cluster analysis

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores ‘interaction types’ used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs.