As automated vehicles (AVs) become integral to urban traffic, understanding multi-pedestrian interaction dynamics is crucial for ensuring safety and efficiency. This study examines how pedestrians' start positions, mutual influence, and AV communication strategies impact crossing behavior and the formation of informal shared spaces. A Virtual Reality (VR) experiment with 30 participants (15 pairs) was conducted, where two participants interacted simultaneously in the same virtual environment alongside a simulated pedestrian and an AV. Pedestrians were assigned different start positions (roadside vs. distant), and the AV employed implicit (rolling stop maneuver) and explicit (eHMI) communication strategies. Results show that distant pedestrians had significantly lower certainty and were less likely to cross in front of the AV than roadside pedestrians. However, over half engaged in joint crossing, exhibiting a significantly shorter crossing time, reinforcing the emergence of informal shared spaces. The rolling stop maneuver effectively discouraged further crossings, enhancing traffic flow but causing some confusion. These findings highlight the need to consider multi-pedestrian interaction dynamics in AV design. Further research should refine AV strategies for navigating informal shared spaces and optimizing pedestrian-AV interactions in complex urban environments.

As we navigate through the evolving landscape of artificial intelligence (AI), human-AI interactions have implications across various fields. Despite significant technical advancements, the interaction aspect remains challenging and continues to impact user trust and acceptance of the systems. Addressing this research gap, this paper introduces an approach for adaptive human-AI interaction or communication, particularly conversational agents. Drawing inspiration from classical rhetoric – ‘ethos, logos and pathos’, we propose a framework to integrate adaptive interaction strategies in the design of AI-enabled conversational agents. The method emphasizes on using a user-centered approach to design adaptive interaction with AI-based conversational agents based on the user and context of use. This paper discusses theoretical concepts, a sample application, and considerations from a human-factors perspective for designing intuitive and meaningful interactions between humans and AI leading to a positive user experience, trust and acceptance of AI-based systems.

Since future mobility will change into mixed traffic, the communication approach of automated vehicles should consider multiple human receivers. This virtual reality study investigates the external communication of an automated vehicle in interactions with multiple pedestrians. Involving 42 participants, the study explores the impact of a simulated pedestrian group at the roadside, a seldom-explored aspect in current research on human-vehicle interactions. Results reveal a significant influence of the additional pedestrian group on participants' behaviors and perceptions, emphasizing the need for nuanced communication strategies in automated vehicle design. This paper contributes to understanding social factors in automated vehicle interactions, providing valuable insights for future research and the development of human-centric automated vehicle communication systems.

Active control inceptors have been used successfully in conventional aviation. However, with the imminent arrival of electric Vertical Take-Off and Landing (eVTOL) aircraft, the use of force feedback as an assistance system should be revisited. In this paper, a concept for a haptic assistance system using an active force feedback sidestick was developed to assist a pilot in precise maneuver execution with an eVTOL. It uses soft stops, detents, tick cues, and stick vibrations to support the take-off, landing, and standard rate turn maneuvers in a generic eVTOL, operating under the Simplified Vehicle Operations (SVO) concept. The concept was validated in a thinking-aloud experiment with N = 10 subject matter experts in a simulator setup. Supporting the use of haptic detents for nominal stick positions and soft stops to indicate system limits, the study also provides insights into further findings on soft stops, tick cues, and vibrations.

Faults in the technical process of automated production systems (aPS) are challenging to detect, report, and recover accurately. Missing physical equipment, lacking detection mechanisms in the control software, and insufficient training and experience of human operators lead to an often delayed and imprecise detection of a fault's root cause. Since fault detection and recovery typically rely heavily on human involvement, there is also a risk of operators performing only partial repairs or inadvertently introducing new faults. Thereby, even slight deviations in human-machine interactions may affect process outcomes unpredictably, which so far cannot be modelled in field-level automation. The Functional Resonance Analysis Method (FRAM) is well-suited to address functional relations in such complex socio-technical systems and accurately represent resource requirements, preconditions, control, and time constraints, making it a promising tool to methodically investigate aPS faults. Yet, the FRAM has barely been applied in this domain. This paper thus introduces an approach relying on the FRAM to methodically derive alarm conditions and system extensions as a foundation for broader uses to investigate in future work. The FRAM-based approach was shown to reduce uncaught faults, required operator interventions, and thus unplanned downtime, in an experiment with two participants on a lab-sized demonstrator machine.

As automation increases qualitatively and quantitatively in safety-critical human cyber-physical systems, it is becoming more and more challenging to increase the probability or ensure that human operators still perceive key artifacts and comprehend their roles in the system. In the companion paper, we proposed an abstract reference architecture capable of expressing all classes of system-level interactions in human cyber-physical systems. Here we demonstrate how this reference architecture supports the analysis of levels of communication between agents and helps to identify the potential for misunderstandings and misconceptions. We then develop a metamodel for safe human machine interaction. Therefore, we ask what type of information exchange must be supported on what level so that humans and systems can cooperate as a team, what is the criticality of exchanged information, what are timing requirements for such interactions, and how can we communicate highly critical information in a limited time frame in spite of the many sources of a distorted perception. We highlight shared stumbling blocks and illustrate shared design principles, which rest on established ontologies specific to particular application classes. In order to overcome the partial opacity of internal states of agents, we anticipate a key role of virtual twins of both human and technical cooperation partners for designing a suitable communication.

The design and analysis of multi-agent human cyber-physical systems in safety-critical or industry-critical domains calls for an adequate semantic foundation capable of exhaustively and rigorously describing all emergent effects in the joint dynamic behavior of the agents that are relevant to their safety and well-behavior. We present such a semantic foundation. This framework extends beyond previous approaches by extending the agent-local dynamic state beyond state components under direct control of the agent and belief about other agents (as previously suggested for understanding cooperative as well as rational behavior) to agent-local evidence and belief about the overall cooperative, competitive, or coopetitive game structure. We argue that this extension is necessary for rigorously analyzing systems of human cyber-physical systems because humans are known to employ cognitive replacement models of system dynamics that are both non-stationary and potentially incongruent. These replacement models induce visible and potentially harmful effects on their joint emergent behavior and the interaction with cyber-physical system components.

We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management.

A mixed traffic consisting of manual and automated vehicles will be part of reality in the near future. Vehicles of higher automation levels, which will have the ability to perform minimal risk maneuvers, will also participate in this traffic. Previous research in this area has focused on the interaction within the automated vehicle and a change of perspective was therefore carried out in this study. Three different maneuvers were evaluated from two perspectives of the surrounding traffic with regard to their controllability. A new method was used to evaluate the driving performance individually. The results of the driving simulator study with 35 participants show that an MRM in the form of a lane change coupled with a standstill on the shoulder lane is the most controllable. The results are consistent from both investigated perspectives.

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.

This study investigated the effects of two text-based framings (‘social’ vs. ‘technical’) of a robot. We aimed to discover whether a robot framed as ‘social’ is perceived as more social and whether it is more persuasive. We used the Desert Survival Problem to create a task the robot and participant solved together. We found no significant differences in robot perception (post interaction), or compliance. Self-reported technical knowledge was not significantly correlated with social attributions to the robot. In line with our hypotheses, perceived competence, perceived helpfulness of the robot, and willingness to work with it again were positively correlated. Furthermore, perceived warmth of the robot was positively correlated with the willingness to work with the robot again.

This paper assesses the usefulness of immersive technology to evaluate perceived discomfort and proxemic behavior towards a robot depending on its size. Therefore, we compared a real and an augmented reality (AR) robot presentation. In a within-subject design, a service humanoid approached participants (N = 32) in four trials in a counterbalanced order. One trial presented a real robot, another showed a same-sized AR version, and two trials showed down-scaled AR versions of the robot. The perceived discomfort and the comfort distance were measured. For the presentation mode comparison, the distance estimation error was measured additionally. The results show that the comfort distance was greater for the AR robot than for the real one. The comfort distance was also greater for the largest robot compared to the smaller sizes, while there was no difference when comparing the smaller ones. There was no difference in perceived discomfort between the presentation modes or the robot sizes. The distance estimation error was greater in AR. The study indicates that results obtained with the AR and real robot are comparable relative to each other. Therefore, utilizing AR could effectively evaluate various versions of robots in terms of the discomfort they induce—a critical prerequisite prior to the manufacturing process. Finally, AR might provide better validity for the evaluation of subjective measures.

As the communication between automated vehicles and human road users becomes increasingly important, this study aimed to investigate the effect of different communication modalities on traffic safety and efficiency. A VR study was conducted wherein 37 participants interacted as pedestrians with an automated vehicle to evaluate implicit communication via lateral offset, explicit communication via an external human-machine interface, and a combination of both. The results indicate that the combined communication approach was ranked the highest in subjective evaluations, while the external human-machine interface achieved the best efficiency results. In terms of traffic safety, no differences were found between the different communication modalities. The study concludes that a combined communication approach, consisting of both implicit and explicit signals, can enable safe and efficient cooperation between traffic participants in future interactions between automated vehicles and pedestrians. However, in situations involving further agents, such as manual vehicles, clearer communication may be necessary. Overall, this study contributes to a better understanding of communication strategies for automated vehicles and provides insights for future research.

In the automotive industry, contact and surface heating systems are designed to enhance thermal comfort in vehicles. The maximum temperature thresholds for such systems are integrated in DIN EN ISO 13732–1. This paper presents a study on the effects of contact surface heating on thermal comfort in automotive interiors, with a particular focus on individuals with heat sensitivity impairments caused by polyneuropathy. Two groups of individuals, one with normal sensitivity and the other with impaired heat sensitivity due to polyneuropathy, were tested for their thermal perception and response to an overwarmed seat and steering wheel heating system. The study aimed to measure the temperature and time at which individuals in a Control and Experimental group would stop using an overwarmed contact surface heating on the steering wheel and seat. In addition, the thermal load on passengers in a vehicle environment was investigated to define time limits and temperature thresholds for comfortable and uncomfortable states when using contact surface heating systems. A BMW driving simulator was used to simulate a realistic in-vehicle experience. The results indicate that both groups could feel the heat but had significant differences in their threshold temperature and time to discontinue heating. The heat sensitivity impaired group had a more sensitive thermal perception compared to the normal sensitive group. The outcomes of this research offer valuable insights for the design and advancement of contact and surface heating systems in vehicles, specifically for individuals who suffer from heat sensitivity impairments.

Cooperation between driver and vehicle automation can be utilized to overcome failures and problems of the automated system. Failures do not always indicate mandatory intervention scenarios but can be within the optional decision space and have a negative impact on user experience. In this paper, we evaluate one user interface (UI) based on a joystick and one based on a touchscreen that enable an increased degree of cooperation compared to state of the art UIs in automated vehicles. Both UIs allow users to adjust parameters and initiate as well as abort maneuvers of the vehicle motion within the optional space of action. In a driving simulator experiment, participants experienced the new concepts in comparison to a state of the art UI. Participants rated both newly developed concepts positively regarding usability and user experience. Overall, they preferred the novel concepts compared to the baseline interface. An objective evaluation of interaction issues and visual distraction revealed flaws that can be resolved by adapting the designs. Participants preferred an increased level of driver vehicle cooperation (DVC) and indicated a high intention to make use of it for optional interventions. The indicated usage intention decreased with increasing automation level.

Advances in artificial intelligence (AI) are leading to an increased use of algorithm-generated user-adaptivity in everyday systems. Explainable AI aims to make algorithmic decision-making more transparent to humans. As future vehicles become more intelligent and user-adaptive, explainability will play an important role in ensuring that drivers understand the AI system’s functionalities and outputs. However, when integrating explainability into in-vehicle features there is a lack of knowledge about user needs and requirements and how to address them. We conducted a study with 59 participants focusing on how end-users evaluate explainability in the context of user-adaptive comfort and infotainment features. Results show that explanations foster perceived understandability and transparency of the system, but that the need for explanation may vary between features. Additionally, we found that insufficiently designed explanations can decrease acceptance of the system. Our findings underline the requirement for a user-centered approach in explainable AI and indicate approaches for future research.

Robots are increasingly involved in tasks that require them to navigate social spaces shared with humans. Following social norms is considered a key requirement for such robots to ensure their acceptance and long-term use. This paper focuses on delivery robots as these often encounter problems in their operational areas–in this case a busy university campus– when they find their way blocked by people and they cannot move on towards their goal destination. We explored automated cue triggering to resolve this situation autonomously without the help of remote operators. Eighty-three pedestrians participated in a real-world study using a delivery robot. Four different cues were tested for their perceived politeness and ambiguity. The four different cues differed in the presence or absence of an instruction and the presence or absence of a justification for the request to let the robot pass as well as source orientation within the justification, which was either internally (self-) directed or externally (user-) directed. The results reveal a complex picture. Overall, a positive effect of verbal instructions in comparison to staying mute on social attributions to the robot was found. Contrary to our expectations, there was no significant difference in politeness between the different requests. Participants’ willingness to let the robot pass was positively correlated with perceived politeness, and negatively correlated with ambiguity of the requests.

Whereas autonomous vehicles are expected to provide several advantages, the current scenarios envisioned for self-driving vehicles are expected to increase the incidence of motion sickness. This study investigates the effects of dynamic visual stimuli on the development of carsickness under two different view conditions. A prototypical light-emitting diode (LED) feedback system visualizing longitudinal driving dynamics in the passenger’s peripheral visual field was installed in the rear of a modified serial vehicle. A real driving experiment was conducted on the test track of a major car manufacturer. Subjective motion sickness ratings were recorded. It was hypothesized that carsickness can be mitigated with the information from the visual feedback system. Subjective motion sickness scores tended to be lower with the LED feedback system while there was no substantial interaction effect with the view condition. Although the results indicate potential benefits of the LED feedback system for the mitigation of motion sickness, further development of the system and its functionalities and the inclusion of psychophysiological measures to objectively quantify motion sickness is necessary to confirm these findings.

In automated driving it is important to ensure drivers’ awareness of the currently active level of automation and to support transitions between those levels. This is possible with$a$suitable human-machine interface (HMI). In this driving simulator study, two visual HMI concepts (Concept$A$and$B$) were compared with$a$baseline for informing drivers about three modes: manual driving, assisted driving, and automated driving. The HMIs, consisting of LED strips on the steering wheel that differed in luminance, color, and pattern, provided continuous information about the active mode and announced transitions. The assisted mode was conveyed in Concept$A$using$a$combination of amber and blue LEDs, while in Concept$B$only amber LEDs were used. During automated driving Concept$A$displayed blue LEDs and Concept B, turquoise. Both concepts were compared to$a$baseline HMI, with no LEDs. Thirty-eight drivers with driving licence were trained and participated. Objective measures (hands-on-wheel time, takeover time, and visual attention) are reported. Self-reported measures (mode awareness, trust, user experience, and user acceptance) from$a$previous publication are briefly repeated in this context (Muthumani et al.). Concept$A$showed 200 ms faster hands-on-wheel times than the baseline, while in Concept$B$several outliers were observed that prevented significance. The visual HMIs with LEDs did not influence the eyes-on-road time in any of the automation levels. Participants preferred Concept B, with more prominent differentiation between the automation levels, over Concept A.

Malfunctions are a major challenge in partially automated driving. During such malfunctions, the driver must be able to adequately take over vehicle guidance without being requested to intervene. This may be particularly difficult in urban areas due to their high complexity and information density. Augmented Reality Head-Up Displays (ARHUDs) may have the potential to support the driver during the monitoring task by providing driving-related information at its required location in the primary field of view. The effects of an ARHUD compared to a Baseline concept in case of malfunctions were investigated in a driving simulation experiment with 52 participants. In a partially automated urban drive, participants experienced a longitudinal and a lateral malfunction in permuted order. The concepts–ARHUD or Baseline– were presented as a between-subject factor. The results showed significantly shorter take-over times when using the ARHUD, resulting in fewer crashes. For those who were able to avoid the crash, no differences in the take-over quality between both concepts were found. There was one difference in visual attention: the attention ratio on the instrument cluster was lower for the ARHUD. In addition, the ARHUD revealed a significantly higher trust and usability rating. However, there were no differences in acceptance and subjective workload between the two concepts. The results showed that the ARHUD has more potential to prevent crashes in the event of malfunctions compared to the Baseline. Nevertheless, the high number of crashes, regardless of the concept, showed the importance of a fallback level for partially automated urban driving.

Standstill behavior by a robot is deemed to be ineffective and inefficient to convey a robot’s intention to yield priority to another party in spatial interaction. Instead, robots could convey their intention and thus their next action via motion. We developed a back-off (BO) movement to communicate the intention of yielding priority to pedestrians at bottlenecks. To evaluate human sensory perception and subjective legibility, the BO is compared to three other motion strategies in a video study with 167 interviewees at the university and public spaces, where it excels regarding legibility. Implemented in a real encounter, objective motion behavior of 78 participants as a reaction to a stop-and-wait strategy, and two versions of BO (short and long), shows an improvement of the pedestrians’ efficiency in the second encounter with the robot’s short BO version compared to the stop strategy. Eventually, in the third encounter with all motion strategies, interaction causes only a small time consumption still required by the cognitive process of perceiving an object in the visual field. Hence, the design of kinematic parameters, BO path and time, exhibits the potential to increase the fluency of an interaction with robots at bottlenecks.

Although an automated vehicle may operate for an extended time, it may suddenly request a user's intervention in critical situations (i.e., beyond the system's operational design domain). Despite the proliferation of studies to understand how users resume control in such critical situations, a systematic analysis of the whole response process is necessary. We analyzed the visual-motor response process of distracted users to front and lateral vehicle conflicts. We also investigated the effect of false warnings and expectations. In a driving simulator experiment (high fidelity, fixed-based, within-subject design), 45 participants performed a visual-manual distracting task until an audio warning was issued. The response process was modeled with Bayesian generalized linear mixed effects models. The models incorporate the carryover effect (up to the 2nd order), typical side effect of within-subjects experiments. Reaction times were modeled with a shifted-Wald distribution; response choices with a softmax regression. The warning was effective at capturing visual attention and prompting the resumption of control, but it did not directly initiate an intervention. Glance location and the choice and timing of evasive maneuver depended on driving context and on previous experience. Analysis of the whole response process yields more relevant information on the effect of warnings for transition of control than a single measure of intervention time. Furthermore, the carryover effect should not be discounted, because trial randomization can only partially alleviate the problem. We provided results in a format that can be used as a reference for future studies and for computational models of driver behavior.

The aim to integrate automated vehicles in manual traffic motivates the investigation of the communication of road users. Especially in situations of high traffic density, an automated lane change without cooperation of interacting traffic participants cannot be executed. Therefore, an automated vehicle needs a distinct interpretable strategy to inform interacting road users of its intention. In this presented driving study, different variations to announce a lane change to interacting traffic are performed on a test track and are evaluated (with 40 participants) in a within-subjects design. To gain standardized situations, all lane change maneuvers are executed automatically. During the study, different factors to announce a lane change, like the time to set the indicator, a weak or strong deceleration to the target gap or a lateral offset in advance of the lane change are investigated. Moreover, the influence of the lane change direction as well as the velocity of the target gap are analysed. The study illustrates that not only an indicator is crucial to announce a lane change. Also a strong deceleration to the target gap influences the processing of the information and cooperation of interacting traffic participants. Other factors, such as a lateral offset in advance of a lane change is evaluated as less important. In addition, the study implies higher cooperation and an influence of the lane change direction at slow velocities.

In the near future, automated vehicles will be integrated in traffic. This will lead to interactions between automated vehicles and drivers of manually driven cars. Due to the fact that with a distracted passenger, communication is only possible to a limited extent, this paper researches the potential of external human-machine interfaces in a road bottleneck scenario in urban traffic. To investigate this capability, a driving simulator study with 46 participants was conducted. The experiment varied the message of the automated vehicle as well as the moment of displaying it. The two concepts consisted of a display, which was mounted at the front of the automated vehicle and a laser projection. The research question this paper addresses is, whether displaying the right of way can support the oncoming human driver in passing through the narrow section of the road. The results show that the passing time of the participants reduces significantly when communicating via an external human-machine interface compared to the condition without an interface.

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition-low), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

Shared vehicle control is a potential intermediate step in the development of fully automated driving. A specific use-case for shared control is a take-over scenario involving conditional automated driving. Such cooperation essentially requires both partner's awareness of the other's intentions. This paper's authors focus on the communication of intent from the system to the driver. The message has to reliably represent the vehicle's intent, and the driver has to correctly interpret it. To investigate this aspect, we designed a driving-simulator experiment. It features an automated system, which recommends an evasive maneuver's direction during the take-over scenario. Drivers confronted four take-over scenarios, three with correct recommendations and one with an incorrect recommendation. We tested the understanding of and trust in the perceived information as well as resulting gaze behavior. The results reveal a mixed interpretation of the information in the first scenario with a shift to mainly correct understanding in the last scenario. Trust was significantly lower only for the case in which an incorrect recommendation was issued. No significant connection between understanding or trust and gaze behavior was found. We conclude that more information about the recommendation's origin should be provided to promote successful ensuing shared control.

The feedback of state transitions and intentions of the automation system is very important for obtaining and/or increasing the driver's awareness of the automation system's state during partially automated driving. In this paper, the feedback is realized via rotational vehicle motions and not, as usual, visually. The detailed design of active pitch motions for feeding back state transitions and intentions of the automation system is evaluated in a driving study on a test track. In doing so, the lateral and longitudinal vehicle guidance is carried out by the automation system.

We investigate the effect of dominant and submissive movement strategies and a movement cue in a human-robot cooperation scenario on perceived predictability and trust. Four different movement strategies in proximal cooperation between a robot manipulator and a human participant were tested in an experiment in which participants had to arrange small objects in a shared workspace working on the same product as the robot. The features of the robot motion were characterized by dominance or a movement cue. The robot modifies its motion in two ways resulting in four different movement strategies: either it stops when the human is in danger of collision (submissive) or not (dominant), and either it performs a backing-off movement cue or not. The participants evaluated the movement strategies in terms of trust and predictability in a questionnaire. We found that the submissive backing-off movement strategy significantly enhanced the users' trust compared to the dominant movement strategy without movement cue. Other strategies showed no significant differences in trust or predictability.

Many novel physical assistance devices are beginning to incorporate intelligent robotic systems and mechatronic components. In terms of a human-centered design it is crucial to assess the perceived subjective usability and acceptance of these systems. A questionnaire was thus designed to evaluate novel physically assisting devices in order to support developers in their design decisions as well as users during individualizing of their assistive devices. Two studies (m = 9, n2 = 21), using two different devices, were conducted to analyze objectivity, reliability, and validity. The results show an overall high internal consistency (Cronbach's α > 0.8), which indicates reliability and applicability of the QUEAD. Criterion validity was tested applying correlations with established objective measures for efficiency (time to task completion), effectivity (errors and collisions), and commitment (mean force). Construct validity was applied using a proposed model and correlations to verify convergence. The results show that the QUEAD is able to assess perceived usability and acceptance.

Human haptic perception can be influenced by interaction with other senses, like vision [1]. The mismatch between visually perceived object size including existing knowledge (top-down) and incoming data via the somatosensory system (bottom-up) can cause illusions at the human operator called size-weight illusion (SWI) [2]. Especially in novel human-robot collaboration systems like cobots [3], where a machine amplifies human strength, this phenomenon may lead to usability and performance issues [4]. The presented study should help to understand human behavior in horizontal manipulation tasks (HMT) such as bimanual imprecise pushing and pulling of heavy loads. Results show that a significant difference in expected strain between a small and a large object does not result in a strong subjective SWI with imprecise HMT operations. However, objective data reveals that, in lower mass conditions, significantly higher forces and force rates were applied while the larger object was present. A possible shared control approach incorporating a Bayesian framework is presented.

Changing industrial, social, and legal situations cause a rethinking of novel production systems. One possible approach is to assist the human workers' power. By application of collaborating robots, weaknesses can be compensated by the strengths of the interacting partner using haptic shared control. To ensure acceptance and well-being on the human side, which can be seen as a condition for high performance, usability of the collaborative systems has to be maximized. This study aims to evaluate usability measures for haptic Human-Robot Collaboration in horizontal manipulation tasks (HMT). Results show significant differences and strong effects for accuracy (standard deviation lateral position, SDLP) and efficiency (time to task completion, TTC) measures in a Human Dyad experiment using a Human-Human Collaboration (HHC) method.

The task of car driving is automated to an ever greater extent. In the foreseeable future, drivers will no longer be required to touch the steering wheel and pedals and could engage in non-driving tasks such as working or resting. Vibrotactile displays have the potential to grab the attention of the driver when the automation reaches its functional limits and the driver has to take over control. The aim of the present literature survey is to outline the key physiological and psychophysical aspects of vibrotactile sensation and to provide recommendations and relevant research questions regarding the use of vibrotactile displays for taking over control from an automated vehicle. Results showed that a distinction can be made between four dimensions for coding vibrotactile information (amplitude, frequency, timing, and location), each of which can be static or dynamic. There is a consensus that frequency and amplitude are less suitable for coding information than location and timing. Vibrotactile stimuli have been shown to be effective as simple warnings. However, vibrations can evoke annoyance, and providing vibrations in close spatial-temporal proximity might cause a lack of comprehension of the signal. We describe the sequential stages of a take-over process and argue that vibrotactile displays are a promising candidate for redirecting the attention of a distracted driver. Furthermore, vibrotactile displays hold potential for supporting cognitive processing and action selection while resuming control of an automated vehicle. Finally, we argue that multimodal feedback should be used to assist the driver in the take-over process.

In the near future, highly automated driving will almost certainly be available in commercial vehicles. Concerning the human-machine interface in such cars, two main issues have to be addressed. First, the detrimental effects of automation have to be avoided. Second, cars should provide an interface that allows the driver to utilize the time while driving highly automated. We conducted a driving simulator study to investigate the concept of geometrical transformation of the steering wheel to address both issues. Therefore, we implemented a prototype steering wheel which changes its shape depending on the current driving mode to improve mode awareness and comfort when driving highly automated. The study was focused on possible negative effects of the mechanical transformation in front of the driver during the take-over process. Results indicate that on average participants reacted faster and took over control later. The number of lane change errors, for example changing lanes without looking into the mirror, even somewhat decreased when using the transforming steering wheel. Furthermore, participants mainly rated the proposed concept as usable without problems during the take-over process.

In traffic conflict techniques, time-to-collision (TTC) is suggested to be a surrogate measure of conflict severity. In order to address the differences in conflict situation between driving cultures, field traffic data have been collected by video recording and image processing at urban midblock crosswalks both in Beijing, China, and Munich, Germany. Focusing on the vehicle-pedestrian (VEH-PED) conflict, by identification of field observation, conflict situation, and TTC calculation, a trajectory-based data matrix is created for understanding the entire conflict process. TTC distribution and relationships between TTC and PED/VEH-based parameters are investigated with intercultural comparison. Special cases in China, where traffic noncompliance is common, are also studied as a complement to normal VEH-PED conflicts. The research will hopefully lay the groundwork in the target of providing advanced driver assistance system adaptation databases in the future.

Traveling with children in tow can pose a serious distraction to the driver, effectively drawing much of the necessary attention away from the road and causing a disruption in normal driving patterns. In this paper we investigate the driver's capacity to operate a vehicle safely when being exposed to a noise stimulus, specifically in the form of a crying baby for an extended period of time. For this purpose, we developed a tailored driving simulator framework to efficiently configure new experiments, built on modular components to make it easier to upgrade and update the experiment scenario and overall conditions. We then compared the driving behavior of parents to individuals without children focusing particularly on the affects on driving performance when a sudden event occurred on the road. We aim to study driving patterns under stressful conditions such as having children as occupants in the vehicle to be able to classify drivers for background training purposes regarding in-vehicle behavior. Results have shown the tendencies of parents when having a baby in the vehicle to produce better driving performances.

Advanced driver assistance systems (ADAS) and driver information systems (DIS) do not always comply with the intended driver safety enhancement. Even if they aim to augment the driver's awareness of the surrounding environment, perceiving this information requires the occasional attention diversion from the road, which could lead to a loss of vehicle control if the total eyes-off-road time exceeds the National Highway Traffic Safety Administration (NHTSA) recommendation for glances away from the roadway. Additionally, technologies that can be found in other mobile environments, smartphones, and tablets are increasingly being integrated into cars, providing a necessary facet of study and continued research in their effects. We addressed this question by analyzing differential preferences for the layout of DIS and ADAS compared with existing ones through a card-sorting experiment. To validate our data, we additionally studied the drivers' performance and gaze with the preferred locations for in-vehicle information through gaze location and speed metrics measurements. Our validation process showed that the time the drivers needed to find the conveyed information in the preferred layout was within the recommended time of the NHTSA Guidelines. Drivers' preferences with regard to the functional layout of current DIS and ADAS compared with existing ones did not essentially differ from the layouts that are currently on the market. However, including mobile applications and social media in a vehicular context was not considered necessary.

Currently, the processes used by many car manufacturers to adapt information from the head unit to the intended display platform are outdated and extremely cumbersome. The individual graphic elements are not designed for use in different contexts and many steps must be done manually to achieve a proper in-vehicle information visualization. Additionally, the amount of data that must be maintained is extremely large, resulting in strong restrictions in the variability of appearance and displayed information. An additional challenge is that multiple car brands belong to the same main company with each brand having a separate identity. Therefore, the graphical user interface (GUI) elements require resizing and recomposition which then reflects the brand's heterogeneous characteristics. To overcome these drawbacks we present a software solution to create and edit flexible, in-vehicle GUIs through reusable elements or widgets that adjust their size and composition to their environmental context in a dynamic and automatic manner. We have examined the quality of the tool through validation rules for each step and proposed calculation algorithms as a possible approach for a largely automated evaluation.

Increased competition between manufacturers made it necessary for them to create products that provide great experiences in order to stand out from the rest of the market. For a positive product evaluation by the user, not only usability, but also user experience plays an important role. Recent research revealed that the most common method of using post-test questionnaires to assess user experience as a remembered episode could miss information about the experience [19]. This study presents a method for measuring user experience through fulfillment of psychological needs momentarily ('momentary UX') in situations instead of in a whole interaction episode. 28 participants used a novel navigation device that is designed to help explore the environment during a leisure car ride as a team together with other passengers. A story approach was used to create a context for the experimental product interaction. The results show that psychological needs relatedness and stimulation addressed by design could be measured specifically and momentarily for relevant events during the interaction. Further, a positive relationship to positive feelings in the interaction was found. Beyond that, a similar questionnaire for the whole episode is validated in this interaction and comparison between episodic and momentary UX is made. It is demonstrated that the method for measuring momentary UX is specific for needs and situations, and possible use cases are discussed.

Tough competition in the automobile market makes it very important to exceed customer expectations with in-vehicle devices or applications in order to stand out in an already saturated market. Research on human-machine interaction (HMI) showed that user satisfaction is not only influenced by usability factors but also by user experience (UX). Since UX in the automotive context has not yet been comprehensively investigated, this article presents a feasible research method for the measurement of UX. We rely on the fulfillment of psychological needs as a user experience measurement and conducted three online surveys to develop a questionnaire for that purpose. We analyze nine need scales, each with 10 items, and reduced the item count afterwards for a shorter form of the questionnaire. Results reveal that the method successfully measured the intended needs. Future work with respect to a further extension and improvement of the method is discussed.

Systems developed to be operated in a vehicular environment have gradually begun to include further applications, which can be found in other mobile environments, such as smart phones and tablets. This continued growth could overwhelm the driver and affect road safety. Thus, it is crucial to ensure that these devices provide the type of information drivers need. This paper focuses on the presentation of in-vehicle information while driving. Function clusters and information prioritization for different modules in Driver Information Systems are primarily investigated through driver preferences analysis. Results are evaluated outlining implications for proper location of information on the in-vehicle displays.

This paper presents a study of the impact of different complex situations on an advanced driver assistance system (ADAS) for anticipatory assistance for the reduction of fuel consumption. Different studies showed that it is possible to reduce the individual fuel consumption of drivers by extending the driver's anticipation horizon through an ADAS. But the influences of the driving situation on the success of such a system has not been researched yet. Therefore the driving simulator study which is presented in this paper deals with the impact of different traffic situations and its complexity on an anticipatory ADAS for fuel reduction in deceleration scenarios. For this, different rural and urban deceleration scenarios where chosen and situations of different complexity were implemented by changing traffic and environmental conditions. As the main focus of the ADAS lies on the reduction of the fuel consumption, this was one of the main variables which was measured. Additionally the glance time on the HMI was analyzed as an indicator for the manner how the system was used. The results showed that the degree of complexity of the chosen road traffic situations has generally no impact on the fuel consumption if it was driven without any assistance system. The glance times on the HMI of the ADAS shorten if a situation is more complex. But this does not lead to differences in the reduction of the fuel consumptions by the ADAS in different complex situations. The overall fuel consumption was reduced by about 10%. These results lead to the assumption that a good designed anticipatory ADAS reduces the driver related fuel consumption independently from the degree of the complexity of a situation.

This article proposes a theory-driven interaction and user interface concept for a cooperative lane-change scenario in a highly automated driving environment. First, cooperative systems are defined and a taxonomy of five levels of cooperation (driver and system intention, mode of cooperation, allocation, interface, and contact) is elaborated. Second, the scenario is introduced and analyzed for each of the levels of cooperation, with a focus on the support of mode awareness, concluded by a detailed analysis based on an interaction diagram. Third, based on this cooperative interaction sequence, a prototypical user interface design is presented, heavily using a scene-linked augmented information display with multimodal amendments.

Detection response tasks (DRTs) have been used in many different laboratory settings and their usage continues to spread. The degree to which the DRT is replicable and produces concurring results is an important characteristic of its validity. The current article presents a comparison of DRT performance across two separate experimental settings. Results are discussed in terms of how the DRTs (specifically: remote or peripheral detection task and tactile detection task) compare with one another, including DRT reaction times in their relative baseline units. Assessments show that DRT results are comparable across different experimental settings, demonstrating the DRT as a valid measurement method.

Viscous friction or damping for rotatory switches is a mechanical parameter for the torque caused by the angular velocity and crucial to human haptic perception. The detection threshold (DT) and just noticeable difference (JND) for this mechanical parameter was measured. The JND was determined for a standard stimulus of 2 mNms. The DT and JND were investigated for two knob sizes and under the presence or absence of an additional task. In the single-task experimental setup the haptic experiment was the only task. In the dual-task experimental setup the Lane Change Test (LCT) was used as additional task. The results indicate that both the DTs and the JNDs do not depend on the presence or absence of an additional task. Surprisingly, the JNDs are not affected by the used knob diameters, as one would expect, whereas the DTs clearly do.

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

A detailed study of cultural differences can facilitate the process of introducing a product into a particular market. Such an analysis can be used to decide to what extend a global design of a product needs to be considered and which subsequent measures related to localization or adaptation to a specific target culture need to be taken. In the particular case of a Driver Information System (DIS), cultural preferences in the interaction with the user interface could lead to a decrease of the ease of navigation through an insufficiently localized human machine interface depending on the culture. In this study we examine cultural differences on the interaction with the menu of a DIS through a cross cultural survey. The web based comparative multilingual questionnaire was analyzed to determine the impact of the user's culture on preferences in the design of the menu structure of a system to be used in a vehicular environment. Results confirm the existence of cultural differences among users from Germany, USA and Japan. In particular we show heterogeneity that affects the menu structure design of the DIS' user interface. The implications for the design and adaptation of entertainment related functions in the DIS' menu structure are outlined.

The increasing traffic volume confronts the road user with a challenging task. The high number of traffic deaths might not be reducible with passive safety alone. However systems that actively influence the guidance of vehicles, like assistance and automation systems, can make a difference towards higher safety, comfort and efficiency. Some of these systems completely take over single subtasks like speed or distance control. This, in turn can lead to effects like "out of the loop", where the driver withdraws from the actual task and even stops monitoring. In order to realize a safe automation system, the project H-Mode follows an approach where both, driver and assistance system are simultaneously affecting the vehicle, whereby the operator is kept in the loop and active. Moreover a haptic-multimodal communication between driver and automation is established by using active interfaces. Regarding this communication alternative control elements, especially two dimensional ones have to be considered.

To minimize the mental workload for the driver and to keep the increasing amount of information easily accessible, sophisticated display and interaction techniques are essential. This contribution focuses on a user-centered analysis for an authoritative grading of head-up displays (HUDs) in cars. Two studies delivered the evaluation data. In a field test, the potential and the usability of the HUD were analyzed. For special driving situations the according display needs and requirements of the users have been identified and compared with in-car displays, so-called head-down displays (HDD). As major result, a high acceptance of the HUD by the driver and a good performance compared to other in-car displays had been reached.

Voice-operated devices are of particular interest in mobile environments, e.g. vehicles. They promise a natural and intuitive interface to devices and services, and they offer hands-free operation, a legal prerequisite for in-car usage in many European countries. Spelling is a common task for the operation of voice operated devices, especially under unfavorable communication conditions. This paper presents a first analysis of the error and fluency rate for 4502 utterances from the German SpeechDat-Car database. The error rate was found to be between 1.7 natural items, and between 3.6 letter sequences. Only 3.6 hesitations. These results suggest that spelling while driving might be a suitable means of fallback interaction if specific error recovery mechanisms are implemented