As autonomous robots become more common in public spaces, spontaneous encounters with laypersons are more frequent. For this, robots need to be equipped with communication strategies that enhance momentary transparency and reduce the probability of critical situations. Adapting these robotic strategies requires consideration of robot movements, environmental conditions, and user characteristics and states. While numerous studies have investigated the impact of distraction on pedestrians’ movement behavior [1]-[4], limited research has examined this behavior in the presence of autonomous robots. This research addresses the impact of robot type and robot movement pattern on distracted and undistracted pedestrians’ movement behavior. In a field setting, unaware pedestrians were videotaped while moving past two working, autonomous cleaning robots. Out of N = 498 observed pedestrians, approximately 8% were distracted by smartphones. Distracted and undistracted pedestrians did not exhibit significant differences in their movement behaviors around the robots. Instead, both the larger sweeping robot and the off-set rectangular movement pattern significantly increased the number of lateral adaptations compared to the smaller cleaning robot and the circular movement pattern. The off-set rectangular movement pattern also led to significantly more close lateral adaptations. Depending on the robot type, the movement patterns led to differences in the distances of lateral adaptations. The study provides initial insights into pedestrian movement behavior around an autonomous cleaning robot in public spaces, contributing to the growing field HRI research.

In this work, an integrative theoretical structure for the propensity to trust (PTT) is derived from literature.In an online study (N ¼ 669), the validity of the structure was assessed and compared in two domains: propensity to trust in humans (PTT-H) and propensity to trust in automated technology (PTT-A).Based on this, an economic scale to measure PTT-A was derived and its psychometric quality was explored based on the first and an additional second study.The observed correlational pattern to basic personality traits supports the convergent validity of PTT-A.Moreover, discriminative predictive validity of PTT-A over PTT-H was supported by its higher relationships to technology-related outcomes.Additionally, incremental validity of PTT-A over basic personality traits was supported.Finally, the internal validity of the scale was replicated in an independent sample and re-test reliability was established.The findings support the added value of integrating PTT-A in research on the interaction with automated technology.

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants’ acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot’s evaluation and effectiveness in everyday situations.

While conversational agents based on Large Language Models (LLMs) can drive progress in many domains, they are prone to generating faulty information. To ensure an efficient, safe, and satisfactory user experience maximizing benefits of these systems, users must be empowered to judge the reliability of system outputs. In this, both disclaimers and agents’ communicative style are pivotal design instances. In an online study with 594 participants, we investigated how these affect users’ trust and a mock-up agent’s persuasiveness, based on an established framework from social psychology. While prior information on potential inaccuracies or faulty information did not affect trust, an authoritative communicative style elicited more trust. Also, a trusted agent was more persuasive resulting in more positive attitudes regarding the subject of the conversation. Results imply that disclaimers on agents’ limitations fail to effectively alter users’ trust but can be supported by appropriate communicative style during interaction.

Drivers want to retain a sense of control when driving (partially) automated vehicles (AVs). Future AVs will continue to offer the possibility to drive manually, potentially leading to challenging driver-initiated takeovers (DITs) due to the "out-of-the-loop problem" and reduced driving performance. A driving simulator study (N=24) was conducted to explore whether cooperative systems, without full control of driving tasks, provide a sense of control to mitigate DITs in varying conflict situations. Conflict levels were operationalized by an AV performing overtaking maneuvers under free, 100m, and 50m visibility on a two-lane rural road. Participants experienced three systems: no intervention-, a cooperative choice-, and a manual control system. Results showed that participants had a similar sense of control with the cooperative system compared to the manual one and preferred it over the manual system. The likelihood of DITs increased with conflict intensity, and trust in the AV moderated the conflict-DIT association.

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.

Due to the technical advances of automated vehicles (AVs), new uncertainties for human road users arise. To overcome these uncertainties, driving strategies of AVs might be aligned to human interaction styles. In vehicle-vehicle interactions, driving behavior is informed by remaining time gaps between vehicles. This video-based experiment investigated the influence of gap sizes and the measurement method on driving decisions. N=32 participants experienced a highly automated drive in which their AV approached narrow passages. The time to arrival (TTA) of the oncoming traffic was varied. Participants had to decide to drive first or second, indicate their decision certainty, and the situation’s criticality. The videos were presented in ascending, descending, and random order. Moreover, participants adjusted the TTA at which they would drive first and second. The results indicated a higher probability of driving first and lower criticality with increasing TTA. Decision certainty was lowest around the 50% threshold, while longer and shorter TTAs resulted in higher certainty. Results differed between the methods. The findings provide guidance for the design of automated systems to mimic human driving behavior.

Automated driving can improve traffic safety as well as the comfort for drivers. The development of automated driving services brings up the need for sufficient sensor data from autonomous vehicles. If the sensor data fails to reach a specific quality limit, the driver needs to act as a fall-back operator to complete the driving task. A more efficient way of dealing with system limitations is the cooperative task-sharing approach, where the driver and the vehicle act cooperatively as long as the system cannot drive in fully automated mode. In this evaluation, we implemented a cooperative speed regulation, where the driver was asked to manually adjust the speed due to the system's recognition failure, while the lateral control was still managed by the vehicle. Three interfaces, a central touch screen, the pedals, and steering wheel buttons, were compared against each other as input modalities in a driving simulator study (N = 36). User experience, suitability for the task, usability, workload, and the conclusive overall rank were evaluated in a within-subjects design. Results indicated that the highly learned pedal interface was rated significantly better than the other two interfaces, although the touch screen interface was rated significantly higher in its hedonic quality. In conclusion, the best known and naturalistic pedal interface should be preferred to design the interaction in driving-related scenarios, while the touch-screen can act as the infotainment control interface.