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

Abstract

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.

Keywords:

• Expectations
• implicit communication
• mixed traffic
• automated vehicles
• manual vehicles
• narrow road passage
• bottom-up processes
• top-down processes
• learning effects

Acknowledgements

We thank Philipp Hock for his (many years of) technical support.

Ethics statement

This research was carried out in accordance with the Declaration of Helsinki and with the consent of the ethical committee of Ulm University (approval no. 507/20). The ethical approval was granted under the condition that the data protection regulations were adhered to. The participants provided their informed consent to participate in the studies.

Author contributions

All authors were involved in the research process.

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data availability statement

The datasets generated for this article will be made available on request to the corresponding author.

Notes

1 In the literature, there is a common distinction between implicit and explicit communication of a vehicle, which, according to Markkula et al. (2020), can be distinguished by whether the behavior of the road user affects his own movement or perception (implicit) or not (explicit). In both cases, the road user behavior “can be interpreted as signalling something to or requesting something from another road user” (p. 741 and p. 742), for instance, slow down to implicitly communicate the intention to let a pedestrian cross the road, or, for the same intention, explicitly flashing the headlights. Regarding explicit communication, there is a vast research field on external human-machine interfaces (eHMIs), that compensate for the absence of an (attentive) driver in AVs (see, e.g., Bengler et al., 2020). However, as research shows that communication between vehicles in traffic is currently mainly implicit and less explicit (e.g., Dey & Terken, 2016; Rettenmaier, Witzig, et al., 2020), in this paper, we only consider driver’s use of implicit vehicle communication and their reaction to it.

Additional information

Funding

This research was conducted within the project “CADJapanGermany: HF” funded by the Federal Ministry of Education and Research of Germany.

Notes on contributors

Linda Miller

Linda Miller is a PhD student at the Human Factors department at Ulm University. Her research interests include human information processing and decision-making in the interaction with intelligent systems, particularly in the context of automated driving and human-robot interaction, aiming to improve safety and well-being for the users.

Johannes Kraus

Johannes Kraus is a postdoctoral researcher at the Human Factors department at Ulm University and head of the subject area “human-robot interaction”. Besides others, he investigates decision-making processes in the interaction with intelligent systems – especially automated vehicles and robots – with a focus on trust, user personality and attitudes.

Ina Koniakowsky

Ina Koniakowsky is a PhD student at BMW Group and Chemnitz University of Technology. She completed her master’s degree in Psychology with a focus on Human Factors at the University of Ulm. Her main research interest is in driver inattention warnings caused by driver monitoring systems.

Jürgen Pichen

Jürgen Pichen is a PhD student at the Human Factors department at Ulm University, working in the field of driver-vehicle cooperation in (partially) automated vehicles. His research interests include innovative interface design, highly automated vehicles, simplification of human-computer interaction, UX, and usability.

Martin Baumann

Martin Baumann is head of the Department of Human Factors at Ulm University. His main research interests are the psychological basis of human-machine interaction in different domains, mainly traffic, human-robot interaction, interaction with intelligent systems, and the development and validation of concepts of cooperative human-machine systems.