An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
In recent years, we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state-of-the-art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This article aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.
Analysis of Interaction Methods in VR Virtual Reality
Virtual Reality is a new revolution in interaction. As a super technology that can trick the human brain, Virtual Reality is widely used in healthcare, education, business, medical, entertainment and industry. In the field of VR, developers are committed to giving users a full and realistic interactive experience, for example by transmitting the senses of touch, smell, sight and hearing to the user's brain and restoring the user's perception of the real world first-hand. This requires not only powerful technology, but also sophisticated interaction design. Many VR giants are currently working on various technologies and systems to enhance the user's interaction experience in the virtual world. This article analyses three of the main interaction methods (motion capture, eye tracking and haptic feedback) and their associated device products and provide a brief comparison and summary of them. Although the interaction methods in VR are not yet unified and the various virtual reality devices on the market have their own flaws, with more and more players getting involved and the rapid development of technology, it is not difficult to judge that in the near future virtual reality interaction devices will explode like a rocket.
Anticipatory driving in automated vehicles: The effects of driving experience and distraction
Objective: To understand the influence of driving experience and distraction on drivers’ anticipation of upcoming traffic events in automated vehicles. Background: In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods: A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results: Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion: In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application: Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.
Comparative Data Analysis of Older Driver’s vs Younger Driver’s Gap Acceptance Behavior at signalized left turns-A driving Simulator Study
Drivers aged 65 and older are particularly prone to motor vehicle crashes, with approximately 20% of traffic fatalities occurring at intersections [11]. Intersections appear to be hazardous for drivers in this age group due to cognitive, perceptual, and psychomotor challenges. Older drivers find it particularly difficult to safely navigate left turns at signalized permissive intersections, having problems adequately detecting, perceiving, and accurately judging the safety of gaps. The increase in the number of elderly drivers has been paralleled by an increase in road-related accidents due to age-related fragility. By 2030, more than 21% of the adult population is projected to be over 65 years old [1]. However, previous studies have not adequately considered the combined effects of the randomized gap, queue length, traffic volume, pedestrians, and physiological factors on driving. The current study aims to address the gap in the literature by explicitly examining older and younger drivers’ gap acceptance behaviors during permissive left turns at four-way intersections. The main objective of this thesis is to study, identify and analyze the effect of Gap Acceptance Behavior on age, traffic volume, queue length, and physiological factors such as heart rate variability (HRV), electrodermal activity (EDA), and motion sickness among older and younger drivers. The data was collected from a driving simulator study comprising 40 participants aged between 20-30 for younger and 65 years for older. The collected data was used for comparative analysis, with the Gap Accepted by the drivers calculated from the video data. The gap is calculated as the distance between the left turning vehicle and the oncoming traffic. All recruited drivers were healthy. Each participant navigated twelve scenarios, six with lower traffic conditions and six with higher traffic conditions. Each lower and higher traffic scenario varied in queue length, with the number of cars in front of the ego vehicle varying from 0, 1, and 2. All varying queue lengths also had one with a pedestrian and another without. The physiological data collected through the Empatica4 wristband was also considered to study the gap acceptance behavior. Another parameter, motion sickness susceptibility score (MSSQ), was obtained from a questionnaire the participants completed after the experiment. Of these factors, queue length, traffic volume, and pedestrians play a significant role in studying gap acceptance. There is a significant difference in accepting and rejecting the gap between young and older drivers. Older drivers’ decision is affected more by factors, such as traffic volume, age, queue length, HRV, EDA, MSSQ score and the presence of pedestrians. This study showed that older drivers exhibited longer gap acceptance times than their younger counterparts while turning left across traffic at permissive intersections. Researchers may use the findings to better understand gap acceptance behaviors, while policymakers may utilize the results to design mobility guidelines.
Conditionally Automated Vehicles as a Safe and Productive Workspace
Conditionally Automated Vehicles (CAVs) are poised to revolutionize transportation by providing not only safe and efficient travel but also the potential to serve as a productive workspace. This dissertation explores the feasibility, design considerations, and potential benefits of utilizing CAVs as mobile workspaces. By examining the safety protocols, ergonomic designs, and productivity tools required, the research aims to establish a framework for integrating work activities into the vehicular environment, thereby enhancing the overall value proposition of CAVs. The findings suggest that with appropriate design and regulatory measures, CAVs can significantly contribute to productivity while maintaining high safety standards.
Correlation between driver visual characteristics and lane change parameters in urban long-term work zone
To investigate the correlation between drivers' visual characteristics and lane change parameters in urban long-term work zone, firstly, this paper obtains the oculomotor parameters of different drivers in urban long-term operation areas through real vehicle experiments and concludes that the pupil area and saccade angel of drivers in warning area and upstream transition area road sections have significant differences within 95% confidence interval by one-way ANOVA method. Next, scatter plots of lane change behavior parameters and eye movement parameters were plotted and validated by the Person correlation statistics method to find that: In the warning zone section, the driver's pupil area was negatively correlated with running speed, distance from the latest lane change point, and lateral displacement acceleration to varying degrees, with the strongest correlation with distance from the latest lane change point, |г|=0.816; the saccade angel was correlated with running speed and lateral displacement acceleration, with the strongest correlation with running speed, |г|=0.667. In the upstream transition zone section, the driver's pupil area was negatively correlated with all three lane change parameters to varying degrees, with the strongest correlation with the distance from the latest lane change point, |г|=0.512; the saccade angel was correlated with the distance from the latest lane change point, |г|=0.538, with no significant correlation with the running speed and lateral displacement acceleration.
Distracted E-Bike Riding among Delivery Workers in China: Prevalence, Analysis, and Mitigation
With the fast development of the online-to-offline market, there are over 13 million delivery workers in China, of which the primary transportation tool is electric bicycles (i.e., e-bikes). Because of the difficulty in executing enforced regulations, delivery workers usually engage in non-riding-related tasks (NRRTs) while riding e-bikes, leading to distracted riding, which can threaten traffic safety. Although distraction engagement in vehicle driving and motorcycle/bicycle riding has been extensively investigated, the prevalence of and the factors leading to distracted riding among delivery workers have not yet been explored. In this study, a survey has been designed to explore the social-psychological factors leading to NRRT engagement among delivery workers. The factors were chosen based on the theory of planned behavior (TPB) framework. We have also assessed workers’ awareness of the regulation in the survey. A total of 150 delivery workers (146 males and 4 females, mean age: 27.25) participated in the study. Results show that delivery workers tended to engage more in technology-based NRRTs, and “manually operating the phone” was the most prevalent. Compared to older peers, younger workers held more positive attitudes toward engaging in NRRTs and perceived engaging in NRRTs as more prevalent among their colleagues. TPB-related factors (i.e., attitudes, descriptive norms, injunctive norms, and perceived behavior control) were found to be correlated with self-reported NRRT engagement. The awareness of the regulations, however, had limited effects in explaining NRRT engagement. This research can provide insights into the design of countermeasures aiming at reducing the prevalence of NRRT engagement among delivery workers.
Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment
This study examines the effects of noise and the use of an Intelligent Virtual Assistant (IVA) on the task performance and workload of office workers. Data were collected from forty-eight adults across varied office task scenarios (i.e., sending an email, setting up a timer/reminder, and searching for a phone number/address) and noise types (i.e., silence, non-verbal noise, and verbal noise). The baseline for this study is measured without the use of an IVA. Significant differences in performance and workload were found on both objective and subjective measures. In particular, verbal noise emerged as the primary factor affecting performance using an IVA. Task performance was dependent on the task scenario and noise type. Subjective ratings found that participants preferred to use IVA for less complex tasks. Future work can focus more on the effects of tasks, demographics, and learning curves. Furthermore, this work can help guide IVA system designers by highlighting factors affecting performance.
Embodied Robot Teleoperation in Construction
The abstract for 'Embodied Robot Teleoperation in Construction' by Tianyu Zhou (2023) from the University of Florida: This dissertation investigates the use of embodied robotic systems for teleoperation in construction environments. The research focuses on the integration of advanced robotics, teleoperation technologies, and construction methodologies to enhance the efficiency, safety, and productivity of construction tasks. Through a series of experimental studies and field trials, the dissertation demonstrates the potential benefits and challenges associated with incorporating teleoperated robots in construction projects. Key findings include improved precision in task execution, reduced risk of human injury, and increased overall project timeliness. The implications of this research suggest a transformative impact on the construction industry's approach to adopting robotic solutions.
