Learning in Mixed Traffic: Drivers' Adaptation to Ambiguous Communication Depending on Their Expectations toward Automated and Manual Vehicles

With the emergence of automated vehicles (AVs), drivers' understanding and expectations of AVs are crucial in their interaction decisions and actions. In a multi-agent driving simulator, participants encountered AVs and manually-driven vehicles (MVs) in a narrow passage. Controlled by a confederate, the vehicles communicated to yield or insist on priority, either distinctly or ambiguously. The ambiguous communication was repeated six times, involving three AVs and three MVs. The results revealed profound differences in expectations toward AVs and MVs, but similar passing times when communication was distinct. However, different learning curves emerged for AVs and MVs. Repeated exposure to ambiguous communication improved passing times for AVs, while no similar improvement was observed for MVs. The study highlights that when distinct bottom-up information is available, the influence of vehicle categories on drivers' behavior is reduced. In turn, top-down processes become more effective when bottom-up information leaves room for interpretation and behavioral adaptation.