Advanced 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.

A calibrated trust level is essential for the safe use of automated systems.In automated driving, overtrust can reduce drivers' monitoring behavior and delay takeover times, which poses significant safety risks.This motivates the need for continuous, objective trust assessment to enable real-time system adaptations.Prior research has identified eye-tracking as a promising approach.Therefore, this study examines the longitudinal evolution of dynamic learned trust as well as its relationship with visual attention.Given that mental models influence both trust and visual attention, their role in this process is also examined 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.The study indicates that the mental model is a key factor within the field of trust evolution and visual attention.This work contributes to advancing trust measurement in automated driving.

With the rapidly advancing capabilities of Level 2 (L2) automated systems the need for driver confirmation of complex maneuvers, such as entering roundabouts, has been increasingly emphasized to ensure active engagement and safety. This study contrasts confirming maneuvers through control gazes with traditional explicit methods like accelerator use to facilitate seamless interaction and promote the adoption of L2 systems. A simulator study involving 64 participants demonstrated a clear preference for the gaze confirmation system, with significantly higher ratings for user experience, acceptance, and intention to use, without compromising trust compared to explicit confirmation. Participants intervened in case of system failures with either system. The results are building a strong case for daring advanced automation via gaze confirmation to harness progress of L2 systems while preserving safety.

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.

New 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.

According 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.

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.

Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.

Future 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.

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.

External 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.

Automated vehicles are pictured as the future of transportation, and facilitating safer driving is only one of the many benefits. However, due to the constantly changing role of the human driver, users are easily confused and have little knowledge about their responsibilities. Being the bridge between automation and human, the human-machine interface (HMI) is of great importance to driving safety. This study was conducted in a static driving simulator. Three HMI designs were developed, among which significant differences in mental workload using NASA-TLX and the subjective transparency test were found. An electroencephalogram was applied throughout the study to determine if differences in the mental workload could also be found using EEG’s spectral power analysis. Results suggested that more studies are required to determine the effectiveness of the spectral power of EEG on mental workload, but the three interface designs developed in this study could serve as a solid basis for future research to evaluate the effectiveness of psychophysiological measures.

Until fully automated vehicles are widely spread, human drivers will remain indispensable. Until then, driving tasks exceeding the operational boundaries of the vehicle would be delegated to drivers by take-over requests, with the driver as a fallback operator. In cooperative approaches, an appropriate interface is essential for driving safety to prevent accidents. In this study, we propose not to rely on the commonly used touchscreen but on the natural habituated interface (NHI) in the form of the steering wheel, which is highly trained during manual driving. Using safety-critical driving scenarios, we compared the safety, usability, and criticality of both interaction approaches in a driving simulator experiment (N = 26). Our results indicate a significantly lower number of dangerous overtakes leading to accidents; and a higher usability score when using the NHI compared to the touchscreen interface.

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.

More and more technical systems enter the vehicle impacting drivers’ experiences. In the human-centered design, an understanding of influencing factors for acceptance and usage is crucial to align in-vehicle technology with the user needs. Addressing the underlying psychological processes, this work modelled drivers’ usage intentions with motivational regulations (SDT), the TAM, and the UTAUT. An online study with 319 German drivers was conducted examining drivers’ positive or negative experiences with assistance and infotainment systems in the vehicle. In linear regressions, the TAM and UTAUT predicted the acceptance equally for assistance and navigation systems. Amotivation, identified regulation, and intrinsic regulation enhanced the prediction of usage intentions by 3.0–15.4% in addition to the UTAUT variables revealing the additional benefit of incorporating the motivational perspective into the modeling of in-vehicle technology acceptance. Future research and practitioners can build upon this theoretical basis and recommendations on improving motivation and well-being.

As autonomous service robots will become increasingly ubiquitous in our daily lives, human-robot conflicts will become more likely when humans and robots share the same spaces and resources. This thesis investigates the conflict resolution of robots and humans in everyday conflicts in the domestic and public context. Hereby, the acceptability, trustworthiness, and effectiveness of verbal and non-verbal strategies for the robot to solve the conflict in its favor are evaluated. Based on the assumption of the Media Equation and CASA paradigm that people interact with computers as social actors, robot conflict resolution strategies from social psychology and human-machine interaction were derived. The effectiveness, acceptability, and trustworthiness of those strategies were evaluated in online, virtual reality, and laboratory experiments. Future work includes determining the psychological processes of human-robot conflict resolution in further experimental studies.

Domestic service robots become increasingly prevalent and autonomous, which will make task priority conflicts more likely. The robot must be able to effectively and appropriately negotiate to gain priority if necessary. In previous human-robot interaction (HRI) studies, imitating human negotiation behavior was effective but long-term effects have not been studied. Filling this research gap, an interactive online study ($N=103$) with two sessions and six trials was conducted. In a conflict scenario, participants repeatedly interacted with a domestic service robot that applied three different conflict resolution strategies: appeal, command, diminution of request. The second manipulation was reinforcement (thanking) of compliance behavior (yes/no). This led to a 3×2×6 mixed-subject design. User acceptance, trust, user compliance to the robot, and self-reported compliance to a household member were assessed. The diminution of a request combined with positive reinforcement was the most effective strategy and perceived trustworthiness increased significantly over time. For this strategy only, self-reported compliance rates to the human and the robot were similar. Therefore, applying this strategy potentially seems to make a robot equally effective as a human requester. This paper contributes to the design of acceptable and effective robot conflict resolution strategies for long-term use.

Human-robot conflicts might occur in the future, for instance, if a robot requests a public resource (e.g., an elevator). It needs to be investigated how the robot's request can be designed acceptably and effectively regarding the robot type, modality, and politeness. In this interactive video-based online study (N = 390), a robot requested priority over an elevator either using a polite or an assertive conflict resolution strategy presented via speech or on the robot's display. The robot was either humanlike, zoomorphic, or mechanoid. The mechanoid robot achieved more compliance than the humanoid robot if it used a verbal command. When the humanoid robot displayed the command instead of using its voice, more participants granted the robot priority. This might indicate that politeness norms are triggered more by a humanoid design and that if a robot makes a command, the modality should match the robot type.

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.

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.

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.

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.

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.

With self-driving vehicles (SDVs), pedestrians lose the possibility of making eye contact with an attentive driver. This study investigated whether an external human-machine interface (eHMI) displaying the automated driving mode (a. without eHMI vs. b. with eHMI) affects how pedestrians respond to different driver’s states: (1) attentive driver, (2) tinted windshield, (3) distracted driver (within-subject design). At a test site, N = 65 pedestrians crossed a pedestrian crossing while a Wizard-of-Oz SDV approached. We assessed perceived safety and crossing onset times after each trial. Findings reveal that without an eHMI, pedestrians felt significantly less safe if the windshield was tinted or the driver was distracted as compared to an attentive driver. With an eHMI, pedestrians did not differ in perceived safety with regard to the driver’s state. We observed no significant differences in pedestrians’ crossing onset times. We conclude that an eHMI helps pedestrians to not consider the driver’s state.

Autonomous vehicles carry the potential to greatly improve mobility and safety in traffic. However, this technology has to be accepted and of value for the intended users. One challenge on this way is the detection and recognition of pedestrians and their intentions. While there are technological solutions to this problem, there seems to be no research on how to make this information transparent to the user in order to calibrate the user’s trust. Our work presents a comparative study of 5 visualization techniques with Augmented Reality or tablet-based visualization technology and two or three information clarity states of pedestrian intention in the context of highly automated driving. We investigated these in a user study in Virtual Reality (N=15). We found that such a visualization was rated reasonable, necessary, and that especially the Augmented Reality-based version with three clarity states was preferred.

Autonomous vehicles (AVs) have the opportunity to reduce accident and injury rates in urban areas and improve safety for vulnerable road users (VRUs). To realize these benefits, AVs have to communicate with VRUs like pedestrians. While there are proposed solutions concerning the visualization or modality of external human-machine interfaces, a research gap exists regarding the AVs’ communication strategy when interacting with pedestrians. Our work presents a comparative study of an autonomous delivery vehicle with three communication strategies ranging from polite to dominant in two scenarios, at a crosswalk or on the street. We investigated these strategies in an online-based video study in a German (N = 34) and a Chinese sample (N = 56) regarding compliance, acceptance and trust. We found that a polite strategy led to more compliance in the Chinese but not the German sample. However, the polite strategy positively affected trust and acceptance of the AV in both samples equally.

Influencing factors on the take-over performance during conditionally automated driving are intensively researched these days. Most of the studies focus on visual and motoric reactions. Only limited information is available about what happens on the cognitive level during the transition from automated to manual driving. Thus, the aim of the study is to investigate a measurement method for assessing the cognitive take-over performance. In this method, the cognitive component decision-making is operationalized via concurrent verbalization of action decisions. The results suggest that valid predictions for the time of the decision can be provided. Additionally, it seems that the effects of situational complexity on the driver behavior can be extended to cognitive processes. A temporal classification of the decision-making within the take-over process is derived that can be applied for the development of cognitive plausible assistance systems.

With 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.

As vulnerable road users, cyclists were often killed or injured because they were not perceived by drivers on rural roads on time. Nowadays, many sensor technology and assistance systems are used to improve traffic safety and efficiency. However, few studies focus on the interaction between drivers and cyclists, especially enhancing drivers' perception of cyclists. Following a cooperative support framework between drivers and vehicles, we designed an interface to improve drivers' perception of cyclists using Augmented Reality (AR) and then evaluated it in the driving simulator. The results show that driving behavior regarding the distance to the cyclist shows a significant change while the driver is supported by the developed AR interface. Consequently, the results implicate that the safety of vulnerable traffic participants can be increased, using a cooperation strategy between the driver and the vehicle. The behaviour while driving manually can be persuaded by the system without any distraction of the driver.

Automated 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.

Automated 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.

Currently 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.

The 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.

Simulator 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. 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.

Driver's uncertainty during decision-making in overtaking results in long reaction times and potentially dangerous lane change maneuvers. Current lane change assistance systems focus on safety assessments providing either too conservative or excessive warnings, which influence driver's acceptance and trust in these systems. Inspired by the emancipation theory of trust, we expect systems providing information adapted to driver's uncertainty states to simultaneously help to reduce long reaction times and build the overall trust in automation. In previous work, we presented an adaptive lane change assistance system based on this concept utilizing a probabilistic model of driver's uncertainty. In this paper, we investigate whether the proposed system is able to improve reaction times and build trust in the automation as expected. A simulator study was conducted to compare the proposed system with an unassisted baseline and three reference systems not adaptive to driver's uncertainty. The results show while all systems reduce reaction times compared to the baseline, the proposed adaptive system is the most trusted and accepted.

Human errors are a major reason for traffic accidents. One of the aims of the introduction of automated driving functions in vehicles is to prevent such accidents as such systems are supposed to be more reliable, react faster with higher precision. Therefore, we assume that an increase of automation features will also increase safety. However, when drivers are not willing to relinquish control to the vehicle, safety benefits of automated vehicles do not take effect. Therefore, convincing drivers to actively make use of the automation when appropriate can increase traffic safety. In this paper we investigate the influence of system feedback in proactive, safety critical takeover situations in automated driving. In contrast to handover, which is initiated by the system, proactive takeover is initiated by the driver, who's intention for steering the car is the reason for driving manually. We compare auditory feedback with audio-visual feedback realized as a virtual co-driver in a user study. We conducted a virtual reality simulator study (n=38) to investigate how system feedback influences the willingness of drivers to relinquish control to the vehicle. There were three conditions of system feedback: in condition none no feedback was given, in condition audio spoken feedback was given, and in condition co-driver additionally to audio feedback, a virtual co-driver on the front passenger seat was displayed. Our research provides evidence that system feedback can lead to an increase of willingness to maintain automation and to follow its safety related advices.

Although there are already fully autonomous vehicles on the roads for testing purposes, a rollout is far away. Autonomous vehicles are still not able to handle everyday driving and remain reliant on the driver when they reach their system limitations. One suggested approach to this problem is handing over the control entirely to the driver, which might become annoying when such situations occur frequently. In contrast, we suggest the usage of cooperative interfaces to avoid full handovers in situations in which the system needs the driver, for instance to approve or monitor a specific maneuver. A driving simulator study with 32 participants revealed that they felt comfortable choosing how the system should handle a situation. They reportedly assessed the situations first instead of relying blindly on the system and were able to handle every situation safely. We report lessons learned regarding cooperative interaction and interfaces, and their in-lab evaluation.

Inspired by the “emancipation” theory of trust, this paper proposes to develop driver's trust in assistance systems based on the assumption that driver's appropriate trust in these systems can be built, when the support of assistance systems is adapted to the drivers' uncertainty state and helps reducing their uncertainty. For example, a trustworthy lane change assistance system is supposed to provide support to the driver during a lane change maneuver by adapting to the state of the driver's uncertainty about distance gaps and closing speeds in respect to the surrounding traffic. The precondition for such a system is a model of driver's uncertainty, which can be used to recognize driver's uncertainty states in lane change situations. This paper mainly presents the development of a probabilistic model for classifying driver's uncertainty in lane change situations. Using experimental data obtained in a simulator experiment, we considered three Bayesian networks: a naive Bayesian classifier, a Tree-Augmented-Naive Bayesian classifier, and a fully connected Bayesian Network. Based on the Bayesian Information Criterion and Accuracy metrics, the Tree-Augmented Naive Bayesian classifier was chosen to predict driver's uncertainty in lane change situations.

Self-driving vehicles are able to drive on their own as long as the requirements of their autonomous systems are met. If the system reaches the boundary of its capabilities, the system has to de-escalate (e.g. emergency braking) or hand over control to the human driver. Accordingly, the design of a functional handover assistant requires that it enable drivers to both take over control and feel comfortable while doing so -- even when they were "out of the loop" with other tasks. We introduce a process to hand over control from a full self-driving system to manual driving, and propose a number of handover implementation strategies. Moreover, we designed and implemented a handover assistant based on users' preferences and conducted a user study with 30 participants, whose distraction was ensured by a realistic distractor task. Our evaluation shows that car-driver handovers prompted by multimodal (auditory and visual) warnings are a promising strategy to compensate for system boundaries of autonomous vehicles. The insights we gained from the take-over behavior of drivers led us to formulate recommendations for more realistic evaluation settings and the design of future handover assistants.