Scholarly Works, Industrial and Systems Engineering

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Research articles, presentations, and other scholarship

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Occupational arm-support and back-support exoskeletons elicit changes in reactive balance after slip-like and trip-like perturbations on a treadmill
Dooley, Stephen; Kim, Sunwook; Nussbaum, Maury A.; Madigan, Michael L. (Elsevier, 2023-11-18)
The purpose of this study was to investigate the effects of arm- and back-support exoskeletons on reactive balance after slip-like and trip-like perturbations on a treadmill. Twenty-eight participants used two arm-support exoskeletons and two back-support exoskeletons with support (i.e., assistive joint torque) activated or deactivated. In each exoskeleton condition, as well in as a control without any exoskeleton, participants were exposed to 12 treadmill perturbations during upright standing. The exoskeletons did not significantly increase the probability of a failed recovery after the perturbations compared to wearing no exoskeleton, but did elicit effects on kinematic variables that suggested balance recovery was more challenging. Moreover, reactive balance differed when wearing back-support and arm-support exoskeletons, and when wearing an activated exoskeleton compared to a deactivated exoskeleton. Together, our results suggest these exoskeletons may increase the risk of slip- and trip-induced falls. The potential mechanisms of this increased risk are discussed and include the added mass and/or motion restrictions associated with wearing these exoskeletons. Our results do not support the assistive hip/back extension moment provided by back-support exoskeletons adversely affecting fall risk.
Pedagogical Design Considerations for Mobile Augmented Reality Serious Games (MARSGs): A Literature Review
Nelson, Cassidy R.; Gabbard, Joseph L. (MDPI, 2023-11-03)
As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002–2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development.
Robots' "Woohoo" and "Argh" Can Enhance Users' Emotional and Social Perceptions: An Exploratory Study on Non-Lexical Vocalizations and Non-Linguistic Sounds
Liu, Xiaozhen; Dong, Jiayuan; Jeon, Myounghoon (ACM, 2023-10)
As robots have become more pervasive in our everyday life, social aspects of robots have attracted researchers' attention. Because emotions play a crucial role in social interactions, research has been conducted on conveying emotions via speech. Our study sought to investigate the synchronization of multimodal interaction in human-robot interaction (HRI). We conducted a within-subjects exploratory study with 40 participants to investigate the effects of non-speech sounds (natural voice, synthesized voice, musical sound, and no sound) and basic emotions (anger, fear, happiness, sadness, and surprise) on user perception with emotional body gestures of an anthropomorphic robot (Pepper). While listening to a fairytale with the participant, a humanoid robot responded to the story with a recorded emotional non-speech sounds and gestures. Participants showed significantly higher emotion recognition accuracy from the natural voice than from other sounds. The confusion matrix showed that happiness and sadness had the highest emotion recognition accuracy, which is in line with previous research. The natural voice also induced higher trust, naturalness, and preference, compared to other sounds. Interestingly, the musical sound mostly showed lower perception ratings, even compared to the no sound. Results are discussed with design guidelines for emotional cues from social robots and future research directions.
Context-Aware Sit-Stand Desk for Promoting Healthy and Productive Behaviors
Hu, Donghan; Bae, Joseph; Lim, Sol; Lee, Sang Won (ACM, 2023-10-29)
To mitigate the risk of chronic diseases caused by prolonged sitting, sit-stand desks are promoted as an effective intervention to foster healthy behaviors among knowledge workers by allowing periodic posture switching between sitting and standing. However, conventional systems either let users manually switch the mode, and some research visited automated notification systems with pre-set time intervals. While this regular notification can promote healthy behaviors, such notification can act as external interruptions that hinder individuals’ working productivity. Notably, knowledge workers are known to be reluctant to change their physical postures when concentrating. To address these issues, we propose considering work context based on their screen activities to encourage computer users to alternate their postures when it can minimize disruption, promoting healthy and productive behaviors. To that end, we are in the process of building a context-aware sit-stand desk that can promote healthy and productive behaviors. To that end, we have completed two modules: an application that monitors users’ computer’s ongoing activities and a sensor module that can measure the height of sit-stand desks for data collection. The collected data includes computer activities, measured desk height, and their willingness to switch to standing modes and will be used to build an LSTM prediction model to suggest optimal time points for posture changes, accompanied by appropriate desk height. In this work, we acknowledge previous relevant research, outline ongoing deployment efforts, and present our plan to validate the effectiveness of our approach via user studies.
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing
Zeng, Yingyan; Chen, Xiaoyu; Jin, Ran (ACM, 2023-10)
An Industrial Cyber-physical System (ICPS) provide a digital foundation for data-driven decision-making by artificial intelligence (AI) models. However, the poor data quality (e.g., inconsistent distribution, imbalanced classes) of high-speed, large-volume data streams poses significant challenges to the online deployment of offline-trained AI models. As an alternative, updating AI models online based on streaming data enables continuous improvement and resilient modeling performance. However, for a supervised learning model (i.e., a base learner), it is labor-intensive to annotate all streaming samples to update the model. Hence, a data acquisition method is needed to select the data for annotation to ensure data quality while saving annotation efforts. In the literature, active learning methods have been proposed to acquire informative samples. Different acquisition criteria were developed for exploration of under-represented regions in the input variable space or exploitation of the well-represented regions for optimal estimation of base learners. However, it remains a challenge to balance the exploration-exploitation trade-off under different online annotation scenarios. On the other hand, an acquisition criterion learned by AI adapts itself to a scenario dynamically, but the ambiguous consideration of the trade-off limits its performance in frequently changing manufacturing contexts. To overcome these limitations, we propose an ensemble active learning method by contextual bandits (CbeAL). CbeAL incorporates a set of active learning agents (i.e., acquisition criteria) explicitly designed for exploration or exploitation by a weighted combination of their acquisition decisions. The weight of each agent will be dynamically adjusted based on the usefulness of its decisions to improve the performance of the base learner. With adaptive and explicit consideration of both objectives, CbeAL efficiently guides the data acquisition process by selecting informative samples to reduce the human annotation efforts. Furthermore, we characterize the exploration and exploitation capability of the proposed agents theoretically. The evaluation results in a numerical simulation study and a real case study demonstrate the effectiveness and efficiency of CbeAL in manufacturing process modeling of the ICPS.
Happiness and high reliability develop affective trust in in-vehicle agents
Zieger, Scott; Dong, Jiayuan; Taylor, Skye; Sanford, Caitlyn; Jeon, Myounghoon (Frontiers, 2023-03)
The advancement of Conditionally Automated Vehicles (CAVs) requires research into critical factors to achieve an optimal interaction between drivers and vehicles. The present study investigated the impact of driver emotions and in-vehicle agent (IVA) reliability on drivers' perceptions, trust, perceived workload, situation awareness (SA), and driving performance toward a Level 3 automated vehicle system. Two humanoid robots acted as the in-vehicle intelligent agents to guide and communicate with the drivers during the experiment. Forty-eight college students participated in the driving simulator study. The participants each experienced a 12-min writing task to induce their designated emotion (happy, angry, or neutral) prior to the driving task. Their affective states were measured before the induction, after the induction, and after the experiment by completing an emotion assessment questionnaire. During the driving scenarios, IVAs informed the participants about five upcoming driving events and three of them asked for the participants to take over control. Participants' SA and takeover driving performance were measured during driving; in addition, participants reported their subjective judgment ratings, trust, and perceived workload (NASA-TLX) toward the Level 3 automated vehicle system after each driving scenario. The results suggested that there was an interaction between emotions and agent reliability contributing to the part of affective trust and the jerk rate in takeover performance. Participants in the happy and high reliability conditions were shown to have a higher affective trust and a lower jerk rate than other emotions in the low reliability condition; however, no significant difference was found in the cognitive trust and other driving performance measures. We suggested that affective trust can be achieved only when both conditions met, including drivers' happy emotion and high reliability. Happy participants also perceived more physical demand than angry and neutral participants. Our results indicated that trust depends on driver emotional states interacting with reliability of the system, which suggested future research and design should consider the impact of driver emotions and system reliability on automated vehicles.
Comparing Self-Report Assessments and Scenario-Based Assessments of Systems Thinking Competence
Davis, Kirsten A.; Grote, Dustin; Mahmoudi, Hesam; Perry, Logan; Ghaffarzadegan, Navid; Grohs, Jacob; Hosseinichimeh, Niyousha; Knight, David B.; Triantis, Konstantinos (Springer, 2023-03)
Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents' judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students' performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.
2nd Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys - Finding Consensus in the Field
Pöhlmann, Katharina; Al-Taie, Ammar; Li, Gang; Dam, Abhraneil; Wang, Yu-Kai; Wei, Chun-Shu; Papaioannou, Georgios (ACM, 2023-09-18)
The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.
Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts
Choe, Mungyeong; Bosch, Esther; Dong, Jiayuan; Alvarez, Ignacio; Oehl, Michael; Jallais, Christophe; Alsaid, Areen; Nadri, Chihab; Jeon, Myounghoon (ACM, 2023-09-18)
This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.
Am I Really Angry? The Influence of Anger Intensities on Young Drivers' Behaviors
Wang, Manhua; Jeon, Myounghoon (ACM, 2023-09-18)
Anger can lead to aggressive driving and other negative behaviors. While previous studies treated anger as a single dimension, the present research proposed that anger has distinct intensities and aimed to understand the effects of different anger intensities on driver behaviors. After developing the anger induction materials, we conducted a driving simulator study with 30 participants and assigned them to low, medium, and high anger intensity groups. We found that drivers with low anger intensity were not able to recognize their emotions and exhibited speeding behaviors, while drivers with medium and high anger intensities might be aware of their anger along with its adverse effects and then adjusted their longitudinal control. However, angry drivers generally exhibited compromised lateral control indicated by steering and lane-keeping behaviors. Our findings shed light on the potentially different influences of anger intensities on young drivers’ behaviors, especially the importance of anger recognition for intervention solutions.
"I See You": Comparing the Effects of Affective Empathy and Cognitive Empathy on Drivers' Affective States and Driving Behavior in Frustrating Driving Contexts
Choe, Mungyeong; Jeon, Myounghoon (ACM, 2023-09-18)
Despite extensive analysis into the relationship between emotion and driving, the effects of empathy on driving remain less explored. This paper focuses on the role of empathy, particularly cognitive and affective empathy, as a potential mitigator of negative emotional states. We investigated how empathic responses from an invehicle agent influence a driver’s emotional state and their driving performance through a between-subject simulation study. Thirty participants were assigned one of three in-vehicle agents: cognitive empathy style, affective empathy style, and non-empathy style agent. They drove using a driving simulator and received empathic responses from in-vehicle agents when adverse events happened. The results showed that affective empathy style in-vehicle agent more helped driver drive safely with lower negative affect states compared to cognitive empathy style agent and no agent.We expect that the findings of this study could provide valuable insight for designing empathic interactions between a driver and a vehicle.
"Play Your Anger": A Report on the Empathic In-vehicle Interface Workshop
Dong, Jiayuan; Nadri, Chihab; Alvarez, Ignacio; Diels, Cyriel; Lee, Myeongkyu; Li, Jingyi; Liao, Pei Hsuan; Manger, Carina; Sadeghian, Shadan; Schuß, Martina; Walker, Bruce N.; Walker, Francesco; Wang, Yiyuan; Jeon, Myounghoon (ACM, 2023-09-18)
Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.
Improving Safety At Highway-Rail Grade Crossings Using In-Vehicle Auditory Alerts
Nadri, Chihab; Lautala, Pasi; Veinott, Elizabeth; Mamun, Tauseef Ibn; Dam, Abhraneil; Jeon, Myounghoon (ACM, 2023-09-18)
Despite increased use of lights, gates, and other active warning devices, crashes still happen at Highway-Rail Grade Crossings (HRGCs). To improve safety at HRGCs, we designed an in-vehicle auditory alert (IVAA) and conducted a multi-site driving simulator study to evaluate the effect of the IVAA on driving behavior at HRGCs. The video shows results of the collaboration between Virginia Tech, Michigan Tech, and the Volpe National Transportation Center recruited a total of N = 72 younger drivers. Driver simulator testing showed that the IVAA improved driving behavior near HRGCs, improving gaze behavior at HRGCs. Drivers looked both ways at crossings more often when the IVAA was present. We expect to run additional tests to further improve the IVAA. Our study can contribute to research efforts targeting driving safety at HRGCs.
How to Ensure Diversity and Inclusion at Conferences? A Workshop for General Chairs, Program Committee Members, Reviewers and Authors
Stojmenova Pečečnik, Kristina; Lee, Seul Chan; Hong, Sara; Schuß, Martina; Şahin Ippoliti, Hatice; Patel, Ankit; Löcken, Andreas; Dey, Debargha; Riener, Andreas; Mirnig, Alexander; Jeon, Myounghoon (ACM, 2023-09-18)
Being the premier forum for automotive user interface research and other vehicular technologies, AutomotiveUI concerns professionals, academics, researchers, and industry representatives from all around the world interested in innovation, research, and application of automotive user interface topics, embodying diversity at its core. This diversity is however not always reflected in the conference’s main program. In order expand the topic foci of the conference in the future, this workshop aims to identify the key factors that influence the main program creation, and create strategies that can help increase its diversity and accessibility, culturally and geographically. We aim to exchange ideas, experiences and start conversations that raise awareness about this topic, in order to inspire longer-term follow-up activities which will eventually result in increased diversity and accessibility not only at AutomotiveUI, but at international conferences in general.
Workshop on Evaluating Augmented Reality in Transportation (EvalAR): A Dialogue Between Researchers and Practitioners
De Oliveira Faria, Nayara; Gabbard, Joseph L.; Burnett, Gary; Meijering, Valerian (ACM, 2023-09-18)
The Workshop on Evaluating Augmented Reality in Transportation (EvalAR) brings together researchers and practitioners to address the challenges of evaluating augmented reality head-up displays (AR HUDs) with safety as a priority. With a collaborative approach, this workshop endeavors to shine a spotlight on the unique features of augmented reality, critically examine existing evaluation practices, and collectively identify future hurdles and actionable solutions. Our overarching goal is to collaboratively establish a strategic roadmap that addresses these challenges over the next 3-5 years and beyond. A key highlight of EvalAR is the introduction of the United Nations Economic Commission for Europe (UNECE) and its Working Party on General Safety Provisions to the AutoUI community. This introduction fosters invaluable collaboration and knowledge exchange, enabling researchers and practitioners to leverage each other’s expertise. By facilitating discussions on knowledge and evidence provision, our workshop aims to bolster the academic community’s contributions to regulatory improvements transportation safety. Furthermore, EvalAR actively explores avenues for alignment with global regulations and industry standards, creating a fertile ground for potential collaborations, funding opportunities, and transformative advancements in augmented reality research for enhanced transportation safety.
Novel In-Vehicle Gesture Interactions: Design and Evaluation of Auditory Displays and Menu Generation Interfaces
Tabbarah, Moustafa; Cao, Yusheng; Abu Shamat, Ahmad; Fang, Ziming; Li, Lingyu; Jeon, Myounghoon (ACM, 2023-09-18)
Using in-vehicle infotainment systems degrades driving performance and increases crash risk. To address this, we developed air gesture interfaces using various auditory displays. Thirty-two participants drove a simulator with air-gesture menu navigation tasks. A 4x2 mixed-model design was used to explore the effects of auditory displays as a within-subjects variable (earcons, auditory icons, spearcons, and no-sound) and menu-generation interfaces as a between-subjects variable (fixed and adaptive) on driving performance, secondary task performance, eye glance, and user experience. The adaptive condition centered the menu around the user’s hand position at the moment of activation, whereas the fixed condition located the menu always at the same position. Results demonstrated that spearcons provided the least visual distraction, least workload, best system usability and was favored by participants; and that fixed menu generation outperformed adaptive menu generation in driving safety and secondary task performance. Findings will inform design guidelines for in-vehicle air-gesture interaction systems.
Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study
Zhao, Yang; Yu, Lisha; Fan, Xiaomao; Pang, Marco Y. C.; Tsui, Kwok-Leung; Wang, Hailiang (MDPI, 2023-09-21)
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users’ multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
Transient use of hemolymph for hydraulic wing expansion in cicadas
Salcedo, Mary K.; Ellis, Tyler E.; Saenz, Angela S.; Lu, Joyce; Worrell, Terrell; Madigan, Michael L.; Socha, John J. (Nature Portfolio, 2023-04)
Insect wings must be flexible, light, and strong to allow dynamic behaviors such as flying, mating, and feeding. When winged insects eclose into adults, their wings unfold, actuated hydraulically by hemolymph. Flowing hemolymph in the wing is necessary for functioning and healthy wings, both as the wing forms and as an adult. Because this process recruits the circulatory system, we asked, how much hemolymph is pumped into wings, and what happens to the hemolymph afterwards? Using Brood X cicadas (Magicicada septendecim), we collected 200 cicada nymphs, observing wing transformation over 2 h. Using dissection, weighing, and imaging of wings at set time intervals, we found that within 40 min after emergence, wing pads morphed into adult wings and total wing mass increased to similar to 16% of body mass. Thus, a significant amount of hemolymph is diverted from body to wings to effectuate expansion. After full expansion, in the similar to 80 min after, the mass of the wings decreased precipitously. In fact, the final adult wing is lighter than the initial folded wing pad, a surprising result. These results demonstrate that cicadas not only pump hemolymph into the wings, they then pump it out, producing a strong yet lightweight wing.
Cro-Create: Weaving Sound Using Crochet Gestures
Bruen, Jacqueline; Kwon, Henry; Jeon, Myounghoon (ACM, 2023-06-19)
Cro-Create is a crochet gesture recognition sonifier for individual and collaborative use. In single user mode, Cro-Create directly scales and maps numerical palm orientation values detected by a motion sensor to sound. In dual user mode, the system affords users with additional auditory feedback by detecting when two users’ gestures are synchronized by segmenting the gestural procedure of making a stitch in crochet into three stages and utilizing a dynamic time warping algorithm to classify and recognize these stages; when the system determines that both users have produced the same gesture, the sonification is complemented by a distinguishable chord. Through this demonstration we introduce our tool to share procedural state for the physical craftmaking process, crochet, through sound.
Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems
Taylor, Skye; Wang, Manhua; Jeon, Myounghoon (Frontiers, 2023-05)
Trust is critical for human-automation collaboration, especially under safety-critical tasks such as driving. Providing explainable information on how the automation system reaches decisions and predictions can improve system transparency, which is believed to further facilitate driver trust and user evaluation of the automated vehicles. However, what the optimal level of transparency is and how the system communicates it to calibrate drivers' trust and improve their driving performance remain uncertain. Such uncertainty becomes even more unpredictable given that the system reliability remains dynamic due to current technological limitations. To address this issue in conditionally automated vehicles, a total of 30 participants were recruited in a driving simulator study and assigned to either a low or a high system reliability condition. They experienced two driving scenarios accompanied by two types of in-vehicle agents delivering information with different transparency types: "what"-then-wait (on-demand) and "what + why" (proactive). The on-demand agent provided some information about the upcoming event and delivered more information if prompted by the driver, whereas the proactive agent provided all information at once. Results indicated that the on-demand agent was more habitable, or naturalistic, to drivers and was perceived with faster system response speed compared to the proactive agent. Drivers under the high-reliability condition complied with the takeover request (TOR) more (if the agent was on-demand) and had shorter takeover times (in both agent conditions) compared to those under the low-reliability condition. These findings inspire how the automation system can deliver information to improve system transparency while adapting to system reliability and user evaluation, which further contributes to driver trust calibration and performance correction in future automated vehicles.

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