Learning from the best – naturalistic arbitration for cooperative driving

Abstract

In cooperative automated driving, the task of lateral and longitudinal vehicle control can be shared by driver and automation. However, conflicting action intentions of the two partners could arise, which need to be resolved within limited time. This can be achieved through structured multimodal negotiation, called arbitration. In order to explore intuitive interaction patterns for arbitration situations, insights from human-human interaction might be transferred. Accordingly, in a field study, couples holding hands or walking arm in arm were videotaped and interviewed when a conflict concerning motion control has been observed. The analysis of the data shows that conflict situations concerning velocity and/or direction of movement occur in natural human-human interaction and that these types of conflict can be dependent on each other. Furthermore, partners use different interaction resources to successfully solve these situations. Results are transferred to cooperative automated driving and an example of an interaction pattern is presented.