Search

As part of MIT's work with the Resources Exploration and Science of OUR Cosmic Environment (RESOURCE) project, in collaboration with NASA Ames and the Solar System Exploration Research Virtual Institute, we investigated the scientific and operational utility of a virtual reality (VR) environment for local, small-scale (<5 m) geological analysis in Lunar and planetary surface exploration missions. A user study was conducted where users explored real environments from Svalbard, Norway, represented in both VR and desktop applications, with the environments mirroring three field sites exhibiting geologic features found on both Earth and Mars. The study aimed to achieve four main objectives: 1) to evaluate if VR improves users' sense of scale, 2) to assess if VR facilitates easier identification of patterns and continuous features, 3) to determine if VR provides a more intuitive method for geological contextualization, and 4) to investigate if VR reduces workload during site exploration. The study, approved by MIT's Institutional Review Board, involved three stages: user training, testing without tools, and geological surveying with tools and fact sheets. Metrics included sense of scale improvement, ease of pattern identification, intuitive geological contextualization, and workload reduction, assessed through questionnaires and the NASA Task Load Index (NASA-TLX). Twenty subjects participated, with tasks randomized across VR and desktop applications. Drone-collected photogrammetry, and environmental data (temperature, humidity and pressure) were collected in Svalbard, Norway, from three distinct geological sites near Longyearbyen. Each site included a water-indicating feature and variation in scale from sub-centimeter to multi-meter. Our comparative study revealed that the VR application provided users with a better sense of scale, improved ability to contextualize geological features, and reduced workload compared to the desktop application. However, while the VR environment enhanced geological contextualization, it showed no significant improvement in the identification of high-level features. We discuss influencing factors for these results and implications for future VR development in geological exploration and astronaut training. • Lessons learned in developing virtual environments for planetary surface exploration and astronaut training. • Field expedition to Svalbard, Norway to collect 3D imagery and environmental sensor data of Mars-relevant geology. • Development of a virtual environment for geological surface exploration of a planetary surface. • User study comparing virtual reality to a desktop application for geological exploration and astronaut training. • Demonstration of improved capabilities using virtual reality for space exploration as a geological field training tool. [ABSTRACT FROM AUTHOR]

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one's constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user's burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized, that recommends any item consistent with the user critiques; and (ii) personalized, which leverages a user's past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system's performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems. [ABSTRACT FROM AUTHOR]

The visualization of complex spatial data is typically achieved using the Windows, Icons, Menus, and Pointer (WIMP) method. However, this approach has limitations, including a steep learning curve, non-intuitive interfaces, and limited visualization capabilities. To overcome these challenges, this study proposes an augmented reality-based (AR-based) visualization and interaction method for complex spatial data. By customizing the rendering pipeline, various visualization effects, such as clipping and lighting adjustments, can be achieved. Additionally, a prototype system called ARSpatialDX is developed. Furthermore, a user study is conducted to compare the AR and WIMP methods. The results indicate that AR outperforms WIMP in terms of virtual-reality matching, perception of physical properties, attractiveness, stimulation, perspicuity, and novelty aspects. Users generally find the direct interaction with data using their hands in AR to be highly interesting and express a strong willingness to continue using it. [ABSTRACT FROM AUTHOR]

Introduction: To avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary. Methods: To investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead. Results: The results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation. Discussion: The results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed. [ABSTRACT FROM AUTHOR]

AbstractSmartwatches are widely used for tracking user physiological and fitness data and are efficient in health management and fitness promotion. However, smartwatch usage exhibits certain negative effects, such as anxiety and dependence. Although the presence of these negative effects has been verified, the widespread existence and degree of these effects on users are not sufficiently investigated. In this study, we explored the tracking anxiety and dependence of 509 smartwatch users based on physiological and fitness data. Our analysis results indicated that users became anxious when their physiological data were abnormal and experienced dependence when tracking sleep status, heart rate, and exercise. Furthermore, tracking anxiety and dependence influenced the smartwatch usage, even causing a few users to consider stopping its usage. Based on these results, we provide design implications for smartwatch designers to improve the user experience of tracking physiological and fitness data. [ABSTRACT FROM AUTHOR]

Avatars play a key role in how persons interact within virtual environments, acting as the digital selves. There are many types of avatars, each serving the purpose of representing users or others in these immersive spaces. However, the optimal approach for these avatars remains unclear. Although consumer applications often use cartoon-like avatars, this trend is not as common in work settings. To gain a better understanding of the kinds of avatars people prefer, three studies were conducted involving both screen-based and virtual reality setups, looking into how social settings might affect the way people choose their avatars. Personalized avatars were created for 91 participants, including 71 employees in the automotive field and 20 participants not affiliated with the company. The research shows that work-type situations influence the chosen avatar. At the same time, a correlation between the type of display medium used to display the avatar or the person's personality and their avatar choice was not found. Based on the findings, recommendations are made for future avatar representations in work environments and implications and research questions derived that can guide future research. [ABSTRACT FROM AUTHOR]

Force feedback is currently missing in most surgical robot systems, even though it would be essential for many surgical tasks, such as palpating tissue. In this study, we used a previously developed robotic endoscope based on series elastic actuation and tested the feasibility of using it for force feedback with a pilot study. The participants had a lower test time (−27%) and lower applied forces (−23%) with force feedback enabled. While further studies are needed for validation in realistic surgical scenarios, these results suggest improved safety and efficiency in telemanipulated surgeries. [ABSTRACT FROM AUTHOR]

This paper investigates the creation of multimodal web interfaces to foster inclusive services, aiming to empower individuals with disabilities. This paper details the development of SEU (Services to Empower yoU), an online platform designed to improve access to offline services for persons with disabilities through personalised multimodal web interfaces. Developed using a participatory design approach, SEU involved collaboration with Human-Computer Interaction experts, occupational therapists, accessibility experts, and persons with disabilities. The platform supports multiple user needs across cognitive, visual, motor, and hearing impairments. SEU was evaluated in a two-phase usability study, which included expert assessment and testing by persons with disabilities, supplemented with interviews, observations, and questionnaires. Feedback highlighted SEU's effective design and usability, emphasising its utility in enhancing service accessibility. The study indicates that both experts and persons with disabilities consider the platform an added value. Although the platform was well-received, suggestions for better cognitive support and the addition of a mobile application were noted, acknowledging the regular smartphone usage among the target users. [ABSTRACT FROM AUTHOR]

We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users' speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (n = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills. [ABSTRACT FROM AUTHOR]

Abstract Avatars play a key role in how persons interact within virtual environments, acting as the digital selves. There are many types of avatars, each serving the purpose of representing users or others in these immersive spaces. However, the optimal approach for these avatars remains unclear. Although consumer applications often use cartoon-like avatars, this trend is not as common in work settings. To gain a better understanding of the kinds of avatars people prefer, three studies were conducted involving both screen-based and virtual reality setups, looking into how social settings might affect the way people choose their avatars. Personalized avatars were created for 91 participants, including 71 employees in the automotive field and 20 participants not affiliated with the company. The research shows that work-type situations influence the chosen avatar. At the same time, a correlation between the type of display medium used to display the avatar or the person’s personality and their avatar choice was not found. Based on the findings, recommendations are made for future avatar representations in work environments and implications and research questions derived that can guide future research.

Real walking is considered as the best locomotion metaphor to explore virtual environments in terms of user experience. In addition to being intuitive for the user, walking captures the true feelings of motion since the visual and proprioceptive sensations are harmonized well. The major disadvantage of choosing walking over other locomotion metaphors involves the physical constraints of the available space, which is usually considerably smaller than the virtual environment. To address this issue, redirected walking (RDW) introduces slight mismatches between a user's visually perceived path and their actual walking pattern, compelling them to subconsciously compensate for the inconsistency by adjusting their walking trajectory. As a result, users are steered to a certain degree, and expansive virtual environments are effectively compressed into smaller physical spaces. Among others, particularly predictive RDW offers immense potential for growth since it unites various algorithmic systems, whereas many approaches from literature depend on drastic restrictions like single-user constraints or architectural limitations to ensure real-time performance. This work presents two novel predictive RDW systems that allow multiple physically colocated users to explore independent and unconstrained virtual environments. The systems rely on two new implementations of prediction systems based on clothoid trajectory generation combined with a cost-based planning concept built on nonharmonic artificial potential fields (APFs), which inherently allow non-convex and dynamic physical environments. Using the APFs, three additional RDW conditions popular in the literature are implemented for comparison purposes. The five RDW concepts are then validated in an extensive user study with 150 participants conducted in 75 pairs. The results indicate that the novel predictive RDW systems outperform the three systems from literature, except for particular sections of the virtual environment with specific architectural traits. [ABSTRACT FROM AUTHOR]

Non-expert users often find it challenging to perceive the reliability of computer vision systems accurately. In human–computer decision-making applications, users' perceptions of system reliability may deviate from the probabilistic characteristics. Intuitive visualization of system recognition results within probability distributions can serve to enhance interpretability and support cognitive processes. Different visualization formats may impact users' reliability perceptions and cognitive abilities. This study first compared the mapping relationship between users' perceived values of system recognition results and the actual probabilistic characteristics of the distribution when using density strips, violin plots, and error bars to visualize normal distributions. The findings indicate that when density strips are used for visualization, users' perceptions align most closely with the probabilistic integrals, exhibiting the shortest response times and highest cognitive arousal. However, users' perceptions often exceed the actual probability density, with an average coefficient of 2.53 times, unaffected by the form of uncertainty visualization. Conversely, this perceptual bias did not appear in triangular distributions and remained consistent across symmetric and asymmetric distributions. The results of this study contribute to a better understanding of user reliability perception for interaction designers, helping to improve uncertainty visualization and thereby mitigate perceptual biases and potential trust risks. [ABSTRACT FROM AUTHOR]

Cinematic virtual reality (CVR) offers filmmakers a wide range of possibilities to explore new techniques regarding movie scripting, shooting and editing. Despite the many experiments performed so far both with both live action and computer-generated movies, just a few studies focused on analyzing how the various techniques actually affect the viewers' experience. Like in traditional cinema, a key step for CVR screenwriters and directors is to choose from which perspective the viewers will see the scene, the so-called point of view (POV). The aim of this paper is to understand to what extent watching an immersive movie from a specific POV could impact the narrative engagement (NE), i.e., the viewers' sensation of being immersed in the movie environment and being connected with its characters and story. Two POVs that are typically used in CVR, i.e., first-person perspective (1-PP) and external perspective (EP), are investigated through a user study in which both objective and subjective metrics were collected. The user study was carried out by leveraging two live action 360° short films with distinct scripts. The results suggest that the 1-PP experience could be more pleasant than the EP one in terms of overall NE and narrative presence, or even for all the NE dimensions if the potential of that POV is specifically exploited. [ABSTRACT FROM AUTHOR]

Safefy-critical domains often employ autonomous agents which follow a sequential decision-making setup, whereby the agent follows a policy to dictate the appropriate action at each step. AI-practitioners often employ reinforcement learning algorithms to allow an agent to find the best policy. However, sequential systems often lack clear and immediate signs of wrong actions, with consequences visible only in hindsight, making it difficult to humans to understand system failure. In reinforcement learning, this is referred to as the credit assignment problem. To effectively collaborate with an autonomous system, particularly in a safety-critical setting, explanations should enable a user to better understand the policy of the agent and predict system behavior so that users are cognizant of potential failures and these failures can be diagnosed and mitigated. However, humans are diverse and have innate biases or preferences which may enhance or impair the utility of a policy explanation of a sequential agent. Therefore, in this paper, we designed and conducted human-subjects experiment to identify the factors which influence the perceived usability with the objective usefulness of policy explanations for reinforcement learning agents in a sequential setting. Our study had two factors: the modality of policy explanation shown to the user (Tree, Text, Modified Text, and Programs) and the "first impression" of the agent, i.e., whether the user saw the agent succeed or fail in the introductory calibration video. Our findings characterize a preference-performance tradeoff wherein participants perceived languagebased policy explanations to be significantly more useable; however, participants were better able to objectively predict the agent's behavior when provided an explanation in the form of a decision tree. Our results demonstrate that user-specific factors, such as computer science experience (p < 0.05), and situational factors, such as watching agent crash (p < 0.05), can significantly impact the perception and usefulness of the explanation. This research provides key insights to alleviate prevalent issues regarding innappropriate compliance and reliance, which are exponentially more detrimental in safety-critical settings, providing a path forward for XAI developers for future work on policy-explanations. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process. [ABSTRACT FROM AUTHOR]

In this research, a blind area information perception and guidance approach for dynamic context is proposed as a solution to the issue of difficult and time-consuming assembly in blind areas. The proposed approach involves the utilization of real-time RGBD data to perceive both blind area context and operator hand information. The resulting data is then used to visualize the blind area scene and provide assembly guidance through the application of augmented reality technology. Unlike conventional methods, the proposed solutions are based on dynamic RGBD data rather than static predefined CAD models, making it simpler to configure and adapt to more scenarios. A user study was designed and conducted to confirm the feasibility of the suggested approach. The results indicate that the suggested approach can decrease assembly time by 49.5%, greatly lower the percentage of assembly errors, reduce the mental load on the workers, and significantly enhance their operational experience. [ABSTRACT FROM AUTHOR]

The European Union is committed to modernising and improving air traffic management systems to promote environmentally friendly air transport. However, the safety-critical nature of ATM systems requires rigorous user testing, which is hampered by the scarcity and high cost of air traffic controllers. In this article, we address this problem with a novel approach that involves non-experts in the evaluation of expert software in an A/B test setup. Using a transformation model that incorporates auxiliary information from a newly developed psychological questionnaire, we predict the performance of air traffic controllers with high accuracy based on the performance of students. The transformation model uses multiple linear regression and auxiliary information corrections. This study demonstrates the feasibility of using non-experts to test expert software, overcoming testing challenges and supporting user-centred design principles. [ABSTRACT FROM AUTHOR]

Summary: Developers spend significant time locating and fixing bugs, which is often performed manually. Although spectrum‐based fault localization (SFL) techniques aim at helping developers to locate faults, they are not yet used in practice. Recent studies have investigated how developers use SFL, presenting different conclusions about their effectiveness and usefulness. We carried out a user study to further enhance the understanding of SFL. We assessed whether SFL improves the developers' performance and to what extent SFL leads developers to inspect faulty code excerpts. We also investigated the intention of the developers to use SFL and how they interact with SFL. Twenty‐six participants performed debugging tasks using real programs, with and without using the Jaguar SFL tool. Using SFL, more developers located and fixed the bugs. SFL also led more developers to inspect the faulty code and locate the faulty method. However, they did not spend less time locating the faults. SFL was well‐accepted by the participants, who showed intention to use it in their daily activities. Our results indicate that SFL is useful even when the fault is not ranked among the first positions, leading developers to reach faulty code regions and find the bugs. [ABSTRACT FROM AUTHOR]

Remote manipulation is a key enabler for upcoming space activities such as in-orbit servicing and manufacture (IOSM). However, due to the large distances involved, these systems encounter unavoidable signal delays, which can lead to poor performance and users adopting a disjointed, "move-and-wait" style of operation. We use a robot arm teleoperated with a haptic controller to test the impact of haptic feedback on delayed (up to 2.6 s: Earth-Moon communications) teleoperation performance for two example IOSM-style tasks. This user study showed that increased latency reduced performance in all metrics recorded. In real-time teleoperation, haptic feedback showed improvements in success rate, accuracy, contact force, velocity, and trust, but, of these, only the improvements to contact forces and moving velocity were also seen at higher latencies. Accuracy and trust improvements were lost, or even reversed, at higher latencies. Results varied between the two tasks, highlighting the need for further research into the range of task types to be encountered in teleoperated space activities. This study also provides a framework by which to explore how features other than haptic feedback can impact both performance and trust in delayed teleoperation. [ABSTRACT FROM AUTHOR]

Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data's alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience. [ABSTRACT FROM AUTHOR]

This paper explores the potential of a German-language chatbot to engage users in argumentative dialogues on ethically sensitive topics. Utilizing an argumentative knowledge graph, the chatbot is equipped to engage in discussions on the ethical implications of autonomous AI systems in hypothetical future scenarios in the fields of medicine, law, and self-driving cars. In a study with 178 student participants, we investigated the chatbot's argumentation effect—its ability to offer new perspectives, gain user acceptance, and broaden users' viewpoints on complex issues. The results indicated a substantial argumentation effect, with 13–21% of participants shifting their opinions to more moderate stances after interacting with the chatbot. This shift demonstrates the system's effectiveness in fostering informed discourse and increasing users' understanding of AI ethics. While the chatbot was well-received, with users acknowledging the quality of its arguments, we identified opportunities for improvement in its argument recognition capabilities. Despite this, our results indicate the chatbot's potential as an educational tool in engaging users with the ethical dimensions of AI technology and promoting informed discourse. [ABSTRACT FROM AUTHOR]

While internet voting is argued to have the potential to improve election processes, concerns about security risks remain one of its main adoption barriers. These concerns are furthermore aggravated by the lack of transparency of internet voting systems that are often perceived as a “black box”. Moreover, there is a research gap in conceptualising transparency and studying voters’ attitudes towards transparency in internet voting. In this work, we aim to address this gap by (1) Conducting a systematic literature review, from which we identified five dimensions of transparency; (2) Developing a questionnaire (Transparency Dimensions of internet voting, TDIV) to assess voters’ attitudes regarding the correlation of these dimensions with transparency; and (3) Conducting an online study (N=500) to investigate voters’ attitudes towards transparency in internet voting. We conclude that providing information about the security of the internet voting system, testing it by independent experts for security vulnerabilities prior to the election, monitoring the election process and verifying its integrity, and providing a remedy for security breaches while holding the responsible parties accountable, are perceived by voters as necessary, and enhance transparency in internet voting systems.

IntroductionTo avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary.MethodsTo investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead.ResultsThe results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation.DiscussionThe results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed.