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Total results: 588

How does navigating with Augmented Reality information affect drivers’ glance behaviour in terms of attention allocation?

Year: 2022

Authors: K Bauerfeind, J Drüke, L Bendewald

Drivers can benefit from Augmented Reality (AR) information especially in ambiguous navigation situations compared to conventional Head-up displays (HUD). AR information is correctly superimposed on the relevant objects in the environment and therefore directly related to the driving situation. Hence, it is assumed, that drivers no longer have to switch glances between the AR information and the environment (Kim & Dey, 2009). It has to be investigated whether switching glances between the presented navigation information and the environment can be reduced with AR information compared to HUD information. Furthermore, the question arises whether AR information might capture drivers’ attention and therefore distract from the traffic situation compared to a HUD as AR information is presented on the driver’s primary visual axis. The aim of the driving simulator study was to examine glance behaviour in terms of attention allocation while participants navigated in an ambiguous left turn situation with an oncoming car in an urban area (N = 58). Hence, drivers were faced with the decision to turn in front of it or let it pass. A conventional HUD and an AR display presented the navigation information to the driver. The drives differed in traffic complexity (low vs. high) to provide indications whether drivers adapt glance behaviour to altered environmental conditions. Besides the navigation task, drivers performed a non-driving-related task to raise drivers’ mental load while navigating. Results showed that with the AR display participants paid more attention to an oncoming car in the ambiguous left turn situation than with the HUD, which indicates that AR information was not distracting. Furthermore, participants switched glances significantly less between the AR navigation information and the environment, which indicates that with the AR display the driver did not have to map the virtual information onto the real driving situation. Independently of the display type 88% of the participants let the oncoming car pass the first time in this situation. Moreover, subjective data showed that drivers benefitted from AR information. The results of this study contribute to the investigation and development of AR user interfaces.

2 versions available

How is emotional resonance achieved in storytellings of sadness/distress?

Year: 2022

Authors: C Rühlemann

Storytelling pivots around stance seen as a window unto emotion: storytellers project a stance expressing their emotion toward the events and recipients preferably mirror that stance by affiliating with the storyteller’s stance. Whether the recipient’s affiliative stance is at the same time expressive of his/her emotional resonance with the storyteller and of emotional contagion is a question that has recently attracted intriguing research in Physiological Interaction Research. Connecting to this line of inquiry, this paper concerns itself with storytellings of sadness/distress. Its aim is to identify factors that facilitate emotion contagion in storytellings of sadness/distress and factors that impede it. Given the complexity and novelty of this question, this study is designed as a pilot study to scour the terrain and sketch out an interim roadmap before a larger study is undertaken. The data base is small, comprising two storytellings of sadness/distress. The methodology used to address the above research question is expansive: it includes CA methods to transcribe and analyze interactionally relevant aspects of the storytelling interaction; it draws on psychophysiological measures to establish whether and to what degree emotional resonance between co-participants is achieved. In discussing possible reasons why resonance is (not or not fully) achieved, the paper embarks on an extended analysis of the storytellers’ multimodal storytelling performance (reenactments, prosody, gaze, gesture) and considers factors lying beyond the storyteller’s control, including relevance, participation framework, personality, and susceptibility to emotion contagion.

6 versions available

How users of automated vehicles benefit from predictive ambient light displays

Year: 2022

Authors: T Hecht, S Weng, LF Kick,K Bengler

With the introduction of Level 3 and 4 automated driving, the engagement in a variety of non-driving related activities (NDRAs) will become legal. Previous research has shown that users desire information about the remaining time in automated driving mode and system status information to plan and terminate their activity engagement. In past studies, however, the positive effect of this additional information was realized when it was integrated in or displayed close by the NDRA. As future activities and corresponding items will be diverse, a device-independent and non-interruptive way of communication is required to continuously keep the user informed, thus avoiding negative effects on driver comfort and safety. With a set of two driving simulator studies, we have investigated the effectiveness of ambient light display (ALD) concepts communicating remaining time and system status when engaged in visually distracting NDRAs. In the first study with 21 participants, a traffic light color-coded ALD concept (LED stripe positioned at the bottom of the windshield) was compared to a baseline concept in two subsequent drives. Subjects were asked to rate usability, workload, trust, and their use of travel time after each drive. Furthermore, gaze data and NDRA disengagement timing was analyzed. The ALD with three discrete time steps led to improved usability ratings and lower workload levels compared to the baseline interface without any ALD. No significant effects on trust, attention ratio, travel time evaluation, and NDRA continuation were found, but a vast majority favored the ALD. Due to this positive evaluation, the traffic light ALD concept was subsequently improved and compared to an elapsing concept in a subsequent study with 32 participants. In addition to the first study, the focus was on the intuitiveness of the developed concepts. In a similar setting, results revealed no significant differences between the ALD concepts in subjective ratings (workload, usability, trust, travel time ratings), but advantages of the traffic light concept can be found in terms of its intuitiveness and the level of support experienced.

7 versions available

Human hand motion prediction based on feature grouping and deep learning: Pipe skid maintenance example

Year: 2022

Authors: T Zhou,Y Wang,Q Zhu,J Du

Human-robot collaboration has gained popularity in various construction applications. The key to a successful human-robot collaboration at the same construction workplace is the delicate algorithm for predicting human motions to strengthen the robot's situational awareness, i.e., robot-human awareness. Most existing approaches have focused on predicting human motions based on repetitive patterns of human behaviors in well-defined task contexts, such as specific object picking tasks, for a relatively short period of time. These methods can hardly capture the 'pattern inconsistency' of human actions, i.e., the differences across people in terms of motion features and even for the same person at different time points of the task. This paper proposes an analytical pipeline that segments and clusters the human inconsistent behaviors into different pattern groups and builds separate human motion pattern prediction models correspondingly. The proposed method, Human Motion Feature Grouping and Prediction (HMFGP), quantifies the spatiotemporal relationship between gaze focus and hand movement trajectories, segments the raw data based on the detected gaze-hand relationship pattern changes, and clusters the matched gaze-hand data segments into several pattern groups based on the pattern similarity of the gaze-hand relationships. Then a time series Deep Learning method is used to predict hand motions based on gaze focus trajectories for each of the pattern groups. The gaze and hand motion data of a human subject experiment (n = 120) for pipe skid maintenance was used to test the prediction performance of HMFGP. The result shows that HMFGP can significantly improve the accuracy of human hand motion prediction and help quantity different patterns of human motions for specific analyses.

1 version available:

Human motion prediction for intelligent construction: A review

Year: 2022

Authors: X Xia,T Zhou,J Du,N Li

Intelligent construction is an important construction trend. With the growing number of intelligent autonomous systems implemented in the construction area, understanding and predicting human motion becomes increasingly important. Based on such predictions, the autonomous systems can optimize their actions to improve the efficiency of human-robot interactions, and supervisors can make informed decisions about when and where to intervene in human motion to avoid collisions. This paper presents a comprehensive review of existing literature on human motion prediction (HMP). Relevant studies from a wide range of fields are reviewed, analyzed and synthesized, in terms of prediction indicators, methods and applications, based on a three-level taxonomy. The taxonomy is structured based on the levels of human information required by different prediction methods, and reflects different understandings of the underlying causality and mediators of human motions and intent. The paper also discusses the evolutions of the theoretical understanding and methodological development of HMP, its application scenarios in and beyond the construction domain, and possible directions for future research. This review is expected to increase the visibility of this rapidly expanding research area, and inspire future studies and advancements for human-robot interactions in construction.

2 versions available

Impact of online courses on University student visual attention during the COVID-19 pandemic

Year: 2022

Authors: Q Gao, S Li

Background: Under the threat of COVID-19, many universities offer online courses to avoid student gatherings, which prevent teachers from collecting responses and optimizing courses. This work collected eye movement data to analyze attention allocation and proposed instruction for improving the courses. Methods: Subjects were recruited to watch three online courses. Meanwhile, their eye movement data were collected through Dikablis Glasses. Mayer’s multimedia cognitive theory was adopted to discriminate the pivotal components of online course, and the Mann–Whitney relevance analysis demonstrated that different representations of courses affected the viewers’ attention differently. Results: Three subjects watched three different types of political courses. Course 1, which combined text and explanation, attracted the most attention. Course 2 was shown to be less attractive than course 1 and better than course 3, but the subjects were distracted by the animations in course 2. Course 3, which did not use any technique to present learning content, attracts the least attention from the subjects. A correlation analysis shows that course 1 and course 3 have similar results compared with course 2. Conclusion: Online courses have become a norm during the COVID-19 pandemic. Improving the quality of online courses can effectively reduce the impact of the epidemic on teaching. These experiment results suggest that text + commentary in the design of online courses can effectively attract the attention of the listeners and achieve better learning results. Attention gradually rises in the early stage and then falls after reaching a peak. At this time, the proper introduction of animation can effectively reverse the attention curve, while individual text or commentary results in quickly losing the listener’s attention.

6 versions available

Interaction strategies with advanced driver assistance systems

Year: 2022

Authors: N Neuhuber,P Pretto,B Kubicek

When using advanced driver assistance systems (ADAS) drivers need to calibrate their level of trust and interaction strategy to changes in the driving context and possible consequent reduction of system reliability (e.g. in harsh weather conditions). By investigating and identifying categories of drivers who choose inadequate interaction strategies, it is possible to address unsafe usage with e.g. tutoring lessons tailored to the respective driver category. This paper presents two studies investigating categories of drivers who apply different interaction strategies when using ADAS. Study I was designed as an exploratory field study with 37 participants interacting with a SAE level 2 system. For the exploratory study, it was important to observe and understand the interaction strategies in a driving context which entails the real complexity of the driving task. The experimental set-up of study II (simulator study), however, allowed to clearly interpret the interaction strategies as either calibrated or un-calibrated by varying the situational risk. Participants (N = 33) were driving in a situation where the system was either working reliably (low-risk condition) or in a situation where the system displayed repeatedly errors under harsh weather conditions (high-risk condition). Cluster analyses with the variables trust, monitoring behavior towards the system and usage behavior were performed to analyze potential categories of drivers. Extreme driver categories with interaction strategies indicative for both misuse and disuse were observed in both studies. In study I, drivers were categorized as either highly trusting attentive, moderately trusting attentive, moderately inattentive, inattentive or skeptical. In study II, drivers were categorized as either un-calibrated, calibrated, inconsistent or skeptical. Taken together, results underline the need of tutoring systems that are tailored for different driver categories.

5 versions available

Interruption management in the context of take-over-requests in conditional driving automation

Year: 2022

Authors: A Borowsky,N Zangi

Drivers of partially automated vehicles are relieved from parts of the driving tasks allocated to the automated driver. This reduction in driving demands encourages them to engage with nondriving related tasks, which may impair awareness of the road environment once a takeover request (TOR) is initiated. This article examined the four suggested strategies drivers that take to regain control following a TOR, from the perspective of interruption management principles. Thirty students participated in a simulated study of two drives, where we manipulated TOR alerts, time to regain control, and potential road hazards. We hypothesized that all four interruption management strategies will be observed. Our hypothesis was confirmed. Four strategies were identified. Most drivers chose strategy 2 to accept and initiate the takeover immediately after the TOR started. The second frequent strategy was to reject the TOR but look at the road. Drivers’ strategy choices changed following alert type and the chronological drive order. With simulated driving experience (i.e., second drive), drivers postponed taking control, adapting to the time budget. Yet, inaccurate understanding of the situation or over-trust affected the chosen strategy. We conclude that interruption management principles are beneficial for studying how drivers respond to TORs and evaluating options to improve TOR performance.

2 versions available

Investigating the Performance of Sensor-Driven Biometrics for the Assessment of Cognitive Workload

Year: 2022

Authors: EK MacNeil

This study investigates the performance of sensor-driven biometrics for the assessment of cognitive workload. Traditional methods for evaluating cognitive workload often rely on subjective self-assessments or take significant time and resources to administer and interpret. Sensor-driven biometrics, such as heart rate variability, skin conductance, and eye tracking, offer a potential alternative. By continuously and non-invasively monitoring physiological responses, these biomarkers can provide real-time insights into cognitive workload. Understanding the relationship between biometric data and cognitive workload can improve efficiency and effectiveness in environments where cognitive demands fluctuate. This research explores the viability of various biometric sensors and analytical techniques to accurately measure and interpret cognitive workload.

3 versions available

Machine Learning Bandwidth Optimization of Interactive Live Free-Viewpoint Multiview Video for Sporting Events

Year: 2022

Authors: RA Kramer

Live free-viewpoint MultiView Video (MVV) allows users to experience their own personalized experience to gaze within a video environment created by a linear array of adjacent cameras that span the playing area of a live sporting event. This technology allows each user to look around as if they are physically at the sporting event. While the broadcast television transport is efficient at transporting the same live video within a one-to-many environment, the broadcast television transport for video does not lend itself to providing each user their own personalized live free-viewpoint MVV content. This dissertation shows that by using machine learning, a broadcast-Internet hybrid system can intelligently predict a population maxima of the viewers’ most desired future free-viewpoint MVV content to transport over the efficient broadcast transport while minimizing the Internet network bandwidth. Accordingly, test results show that by using machine learning, overall bandwidth efficiency is significantly improved while meeting each viewer’s personalized free-viewpoint MVV content demands. Notably, the test results show that by using machine learning, overall bandwidth efficiencies of 91+% to 98+% are obtainable using the broadcast transport alone. Moreover, this dissertation includes (1) machine learning algorithm implementation details, and (2) test results that show overall system bandwidth efficiency improvements based on the use of actual ground-truth soccer video and dataset data over a wide range of worst-case conditions. Overall, this dissertation demonstrates that machine learning may be used to learn the characteristics of sporting events to meaningfully improve overall system bandwidth efficiency for the transport of personalized free-viewpoint MVV content.

1 version available: