Volume 63, Issue 2 - User Interfaces for Automated Vehicles / Guest Editors: Bastian Pfleging, Shadan Sadeghian, Debargha Dey, Host Editor: Andreas Butz

Fully automated drive still lies far ahead in the future. Therefore, vehicles with multiple modes of operation will not disappear fully as many road types, traffic and weather conditions will not allow fully automated drive. Instead, fragmented trips with regard to automation will prevail, where drivers will have different levels of automation available at different times. Given this scenario and the complexity of vehicles offering multiple levels of automation with different driving modes depending on prevailing conditions, the need for drivers to understand their responsibility during the different modes becomes critical. The aim of this paper is to contribute to further understanding of how perceived control influences the driver’s mode awareness of and responsibility for the driving task by reporting on an on-road Wizard-of-Oz study under real driving conditions. The results show that when confronted with a vehicle offering both a level 2 and a level 4 driving automation system, drivers have difficulty in determining whether control is allocated to them or to the system. Further results show that perceived control and responsibility for the driving task are closely linked, and that the driver’s perception of the driving system influence how they interact with it. Finally, conclusions are drawn regarding the way perceived control influences mode awareness when interacting with a vehicle that features multiple levels of automation.

In automated driving, getting ready to drive after a take-over is one of the most crucial topics. Whereas previous research mainly focuses on behavioral data, little is known about the driver’s experience. In this simulator study, the participants are asked retrospectively when they felt ready to drive again after the take-over. The results suggest that driver availability is a subjectively and situationally influenced concept determined by motoric, temporal, visual, or cognitive factors. Identifying the relevant factors contributes to the development of tailored support during the transition.

To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver (n=8n=8) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

The inappropriate use of automation as a result of trust issues is a major barrier for a broad market penetration of automated vehicles. Studies so far have shown that providing information about the vehicle’s actions and intentions can be used to calibrate trust and promote user acceptance. However, how such feedback could be designed optimally is still an open question. This article presents the results of two user studies. In the first study, we investigated subjective trust and user experience of (N=21) participants driving in a fully automated vehicle, which interacts with other traffic participants in virtual reality. The analysis of questionnaires and semi-structured interviews shows that participants request feedback about the vehicle’s status and intentions and prefer visual feedback over other modalities. Consequently, we conducted a second study to derive concrete requirements for future feedback systems. We showed (N=56) participants various videos of an automated vehicle from the ego perspective and asked them to select elements in the environment they want feedback about so that they would feel safe, trust the vehicle, and understand its actions. The results confirm a correlation between subjective user trust and feedback needs and highlight essential requirements for automatic feedback generation. The results of both experiments provide a scientific basis for designing more adaptive and personalized in-vehicle interfaces for automated driving.

External Human-Machine Interfaces (eHMIs) are expected to bridge the communication gap between an automated vehicle (AV) and pedestrians to replace the missing driver-pedestrian interaction. However, the relative impact of movement-based implicit communication and explicit communication with the aid of eHMIs on pedestrians has not been studied and empirically evaluated. In this study, we pit messages from an eHMI against different driving behaviors of an AV that yields to a pedestrian to understand whether pedestrians tend to pay more attention to the motion dynamics of the car or the eHMI in making road-crossing decisions. Our contributions are twofold: we investigate (1) whether the presence of eHMIs has any objective effect on pedestrians’ understanding of the vehicle’s intent, and (2) how the movement dynamics of the vehicle affect the perception of the vehicle intent and interact with the impact of an eHMI. Results show that (1) eHMIs help in convincing pedestrians of the vehicle’s yielding intention, particularly when the speed of the vehicle is slow enough to not be an obvious threat, but still fast enough to raise a doubt about a vehicle’s stopping intention, and (2) pedestrians do not blindly trust the eHMI: when the eHMI message and the vehicle’s movement pattern contradict, pedestrians fall back to movement-based cues. Our results imply that when explicit communication (eHMI) and implicit communication (motion-dynamics and kinematics) are in alignment and work in tandem, communication of the AV’s yielding intention can be facilitated most effectively. This insight can be useful in designing the optimal interaction between AVs and pedestrians from a user-centered design perspective when driver-centric communication is not available.