Long-Term Evolution of Driver Visual Attention during Automated Driving in Real-Traffic: Investigating the Influence of Mental Model and Dynamic Learned TrustA calibrated trust level is essential for the safe use of automated systems. In automated driving, overtrust can reduce the driver’s monitoring behavior and delay takeover times, which poses significant safety risks. This motivates the need for continuous, objective trust assessment for real-time system adaptations. Prior research identified eye-tracking as a promising approach. Therefore, this study examines the longitudinal relationship between dynamic learned trust and visual attention. Given that mental models influence both trust and visual attention, their role in this process is also explored over time. In a longitudinal study, twenty-three participants repeatedly operated an automated vehicle in real traffic while their visual attention was recorded via the vehicle’s built-in driver monitoring camera. Findings suggest an interrelation between dynamic learned trust and mental model formation, with mental models mediating the effect of dynamic learned trust on visual attention. This work contributes to advancing trust measurement during automated driving.2025SSStephanie Seupke et al.Automated Driving Interface & Takeover DesignEye Tracking & Gaze InteractionAutoUI
Unraveling Subjective ADAS Comprehension Considering Factors of Situational Complexity on the Example of Traffic Light ScenariosAdvanced driver assistance systems (ADAS) with increasing automation maturity and availability in urban contexts are entering the market. Meanwhile, the situational context has been identified to play a crucial role in system comprehension and usage, yet its subcomponents and their relation to system comprehension remain an open research question. To gain insights in the role of the situation complexity regarding subjective system comprehension and different methodological aspects, this study applies a mixed quantitative and qualitative approach, focusing on signaled intersections as an exemplary scenario. An on-road study with forty-six participants was conducted, involving six traffic light scenarios (all experienced twice). Results indicate that while comprehension was generally high, the situational context, including environmental and traffic-related factors, affected subjective system understanding. The proposed approach sheds light on the role of mixed methods in ADAS research, which may provide insights for system developers and suggestions for user training content.2025CBClaudia Buchner et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
Exploring Urban Challenges: Understanding Advanced Driver Assistance Systems in Different Situational ContextsNew Advanced Driver Assistance Systems (ADAS) are now available to support urban driving. To adequately use ADAS, especially in complex situations, drivers must comprehend them. An on-road study was conducted to investigate the mental model development while interacting with a state-of-the-art ADAS in both a rural (less complex) and an urban context (more complex). Forty-six participants experienced two rounds of each context. After each round, drivers rated their mental model, acceptance, and trust. Results indicate that for the rural context participants learned the system functionality in the first round without further improvement . In the urban context the mental model was generally less accurate, but improved in the second round. Trust increased from the first to the second rural round while acceptance did not show a significant change within the context. The results provide a first glimpse into the importance of evaluating different contexts and interaction scenarios for ADAS.2024CBClaudia Buchner et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
Improving Driver Engagement with Level 2 Automated Systems: The Impact of Fully Shared Longitudinal ControlAccording to the Society of Automotive Engineers (SAE), in Level 2 systems (L2 systems), the system executes the longitudinal and lateral control of the vehicle, with the driver required to monitor the environment and intervene when necessary. To further improve safety and driver engagement, we compared a fully shared longitudinal control system, which permits speed adjustments via acceleration and braking without deactivation, with a conventional system that disengages upon braking. In a simulator study involving 61 participants, both systems were well-received in terms of acceptance and user experience. The fully shared longitudinal control led to more frequent and earlier braking, suggesting anticipatory driving, without compromising perceived safety. Furthermore, it outperformed in hedonic qualities of user experience, and elicited a stronger intention to use. Our findings indicate that fully shared longitudinal control can enhance driver engagement, offering a valuable improvement for L2 automated systems.2024JIJohannes Illgner et al.Automated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
Empowering Calibrated (Dis-)Trust in Conversational Agents: A User Study on the Persuasive Power of Limitation Disclaimers vs. Authoritative StyleWhile 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.2024LMLuise Metzger et al.Ulm UniversityConversational ChatbotsHuman-LLM CollaborationExplainable AI (XAI)CHI
I've Got the Power: Exploring the Impact of Cooperative Systems on Driver-initiated Takeovers and Trust in Automated Vehicles in Conflicting SituationsDrivers 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.2023MWMarcel Woide et al.Automated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationAutoUI
Exploring Driver Responses to Authoritative Control Interventions in Highly Automated DrivingFuture automated driving systems (ADS) are discussed as having the ability to "override" driver control inputs. Yet, little is known about how drivers respond to this, nor how a human-machine interaction (HMI) for them should be designed. This work identifies intervention types associated with an ADS that has change control authority and outlines an experiment method which simulates a deficit in driver situation awareness, enabling the study of their responses to interventions in a controlled environment. In a simulator study (N = 18), it was found that drivers express more negative valence when their control input is blocked (p = .046) than when it is taken away. In safety-critical scenarios, drivers respond more positively to interventions (p = .021) and are willing to give the automation more control (p = .018). An experimental method and HMI design insights are presented and ethical questions about the development of automated driving are provoked.2023LDLiza Dixon et al.Automated Driving Interface & Takeover DesignAutoUI
Interaction Effects of Pedestrian Behavior, Smartphone Distraction and External Communication of Automated Vehicles on Crossing and Gaze BehaviorExternal communication of automated vehicles is proposed to replace driver-pedestrian communication in ambiguous crossing situations. So far, research has focused on simpler scenarios with one attentive pedestrian and one automated vehicle. This virtual reality study (N=115) investigates a more complex scenario with other crossing pedestrians, a distracting task on the smartphone, and external communication by the automated vehicle. Interaction effects were found for crossing duration, gaze behavior, and subjective measures. For attentive pedestrians, the external communication resulted in shorter crossing durations, higher perceived safety, as well as lower perceived criticality, cognitive workload, and effort. These positive effects were not found when pedestrians were distracted. Instead, distracted pedestrians benefited from other crossing pedestrians because they looked less at the stopping vehicle, felt safer, perceived the situation as less critical, and reported lower cognitive workload and effort. Pedestrians initiated crossings earlier with a group or external communication and later with a smartphone.2023MLMirjam Lanzer et al.Ulm UniversityExternal HMI (eHMI) — Communication with Pedestrians & CyclistsTeleoperated DrivingCHI
From SAE-Levels to Cooperative Task Distribution: An Efficient and Usable Way to Deal with System Limitations?Automated driving seems to be a promising approach to increase traffic safety, efficiency, and driver comfort. The defined automation capability levels (SAE) recommend a distinct takeover of the vehicle's control from the human driver. This implies that if the system reaches a system boundary, the control falls back to the human. However, another possibility might be the cooperative approach of task distribution: The driver provides the missing information to the automation, which will stay activated. In a driving simulator study, we compared both a classical and a cooperative approach (N = 18). An automated car was driving on a rural road when a slower leading vehicle made it impossible for the automation to overtake. The participants could either initiate the overtake by providing the missing information cooperatively or fully taking over the vehicle's control. Results showed that the cooperative approach has a higher usage and reduces workload. Therefore, the suggested cooperative approach seems to be more promising.2021JPJürgen Pichen et al.Automated Driving Interface & Takeover DesignAutoUI
Calibrating Pedestrians' Trust in Automated Vehicles: Does an Intent Display in an External HMI Support Trust Calibration and Safe Crossing Behavior?Policymakers recommend that automated vehicles (AVs) display their automated driving status using an external human-machine interface (eHMI). However, previous studies suggest that a status eHMI is associated with overtrust, which might be overcome by an additional yielding intent message. We conducted a video-based laboratory study (N=67) to investigate pedestrians’ trust and crossing behavior in repeated encounters with AVs. In a 2x2 between-subjects design, we investigated (1) the occurrence of a malfunction (AV failing to yield) and (2) system transparency (status eHMI vs. status+intent eHMI). Results show that during initial encounters, trust gradually increases and crossing onset time decreases. After a malfunction, trust declines but recovers quickly. In the status eHMI group, trust was reduced more, and participants showed 7.3 times higher odds of colliding with the AV as compared to the status+intent group. We conclude that a status eHMI can cause pedestrians to overtrust AVs and advocate additional intent messages.2021SFStefanie Martina Faas et al.Mercedes-Benz AG, Ulm UniversityExternal HMI (eHMI) — Communication with Pedestrians & CyclistsCHI
A Longitudinal Video Study on Communicating Status and Intent for Self-Driving Vehicle Pedestrian InteractionWith self-driving vehicles (SDVs), pedestrians cannot rely on communication with the driver anymore. Industry experts and policymakers are proposing an external Human-Machine Interface (eHMI) communicating the automated status. We investigated whether additionally communicating SDVs' intent to give right of way further improves pedestrians' street crossing. To evaluate the stability of these eHMI effects, we conducted a three-session video study with N=34 pedestrians where we assessed subjective evaluations and crossing onset times. This is the first work capturing long-term effects of eHMIs. Our findings add credibility to prior studies by showing that eHMI effects last (acceptance, user experience) or even increase (crossing onset, perceived safety, trust, learnability, reliance) with time. We found that pedestrians benefit from an eHMI communicating SDVs' status, and that additionally communicating SDVs' intent adds further value. We conclude that SDVs should be equipped with an eHMI communicating both status and intent.2020SFStefanie M. Faas et al.Mercedes-Benz AG & Ulm UniversityAutomated Driving Interface & Takeover DesignExternal HMI (eHMI) — Communication with Pedestrians & CyclistsCHI
Cooperative Overtaking: Overcoming Automated Vehicles' Obstructed Sensor Range via Driver HelpAutomated vehicles will eventually operate safely without the need of human supervision and fallback, nevertheless, scenarios will remain that are managed more efficiently by a human driver. A common approach to overcome such weaknesses is to shift control to the driver. Control transitions are challenging due to human factor issues like post-automation behavior changes. We thus investigated cooperative overtaking wherein driver and vehicle complement each other: drivers support the vehicle to perceive the traffic scene and decide when to execute a maneuver whereas the system steers. We explored two maneuver approval and cancel techniques on touchscreens, and show that cooperative overtaking is feasible, both interaction techniques provide good usability and were preferred over manual maneuver execution. However, participants disregarded rear traffic in more complex situations. Consequently, system weaknesses can be overcome with cooperation, but drivers should be assisted by an adaptive system.2019MWMarcel Walch et al.Automated Driving Interface & Takeover DesignTeleoperated DrivingAutoUI
Towards Opt-Out Permission Policies to Maximize the Use of Automated DrivingAutomated driving has the potential to reduce road fatalities. However, the public opinion to use automated driving can be described as skeptical. To increase the use of automated driving features, we investigate the persuasion principle of opt-out permission policies for enabling the automation, meaning automatically enabling the automation if users do not veto. In a driving simulator study (n = 19), participants drove on three different tracks (city, highway, rural). Three different interface concepts (opt-out, opt-in, control) were examined regarding their effects on automation use, trust, and acceptance. We found that an opt-out activation policy may increase automation usage for some participants. However, opt-out was perceived as more persuasive and more patronizing than the other conditions. Most importantly, opt-out can lead to mode confusion and therefore to dangerous situations. When such an opt-out policy is used in an automated vehicle, mode confusion must be addressed.2019PHPhilipp Hock et al.Automated Driving Interface & Takeover DesignAutoUI
How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving - Review and Hands-On ConsiderationsSimulator studies have been conducted in the automotive domain since the 1960s. Recently, automated driving studies have become more popular as real-world automated cars start to emerge but at this time not all levels of automation can be realized. But as a simulation does not entail all details of real driving, creating a realistic simulation experience - both on a psychological and physical level - proposes recurring challenges. These are among others: sample acquisition, simulator sickness, simulator training, interface design, take-over requests and secondary tasks in automated driving simulator studies. In this paper, we review existing literature and summarize important lessons from simulations in the domain of driving automation to provide considerations for studies investigating driver behavior in the age of highly automated driving.2018PHPhilipp Hock et al.Automated Driving Interface & Takeover DesignAutoUI
P1 - Design Guidelines for Reliability Communication in Autonomous VehiclesCurrently offered autonomous vehicles still require the human intervention. For instance, when the system fails to perform as expected or adapts to unanticipated situations. Given that reliability of autonomous systems can fluctuate across conditions, this work is a first step towards understanding how this information ought to be communicated to users. We conducted a user study to investigate the effect of communicating the system's reliability through a feedback bar. Subjective feedback was solicited from participants with questionnaires and semi-structured interviews. Based on the qualitative results, we derived guidelines that serve as a foundation for the design of how autonomous systems could provide continuous feedback on their reliability.2018SFSarah Faltaous et al.Automated Driving Interface & Takeover DesignAutoUI
Calibration of Trust Expectancies in Conditionally Automated Driving by Brand, Reliability Information and Introductionary Videos: An Online StudyThe design of a priori information about a conditionally automated driving (CAD) function influences the extent of effective usage of this function. The present online study investigated the effects of preliminary reliability and brand information on trust and acceptance for CAD. N = 519 participants were randomly assigned to (1) a reliability condition (high or low) and (2) an original equipment manufacturer (OEM) reputation condition (i.e., above average, average, below average, baseline). To measure the effect of CAD experience, participants were additionally exposed to four short videos of a driver interacting with a CAD function. Study results provide first evidence for an influence of OEM branding and reliability on CAD evaluation. We observed a trend towards more favorable attitudes for high compared to low reliability. This effect depends on the respective OEM reputation. The findings hold implications for the design of communication on automated vehicles to calibrate a priori assessment.2018YFYannick Forster et al.Automated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationAutoUI