Publication Hub

Where to gaze during take-over: eye gaze strategy analysis of different situation awareness and hazard perception levels

While autonomous vehicles are being developed for the future of surface transportation, drivers today still need to be prepared for takeover. The objective of this study is to understand the optimal gaze strategy during the take-over process. First, an affine transfer method was used to link the eye tracking coordinates and pre-defined Aera-of-Interests (AOIs) locations. Then, independent t-tests were applied to analyze the relevance between the gaze strategy determined by the gaze time percentages on various AOIs and the Situation Awareness (SA) and Hazard Perception (HP) levels. The results showed that drivers with higher SA used different gaze strategies before and after they detected the hazards, while drivers with higher HP kept focusing on the center of the road. Explanations and implications of take-over request design are discussed.

Eye Tracking Glasses

Software

4 versions available

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.

Eye Tracking Glasses

Software

10 versions available

A QE Study on Golf Putting

[목적] QE는 주요 움직임 직전에 나타나는 시선고정시간이다. QE는 숙련자가 운동기술을 사용할 때 나타나는 특징을 가지고 있다. 본 연구의 목적은 골프 퍼팅에서의 QE 원인을 규명하고 초보자에게 퍼팅 수행 향상을 위한 연습 방법을 제시하는 것이다. [방법] 연구대상자(n=30)는 통제, QE, 시각차단 집단으로 각각 10명씩 무선 배정하였고 집단 별 연습 방법에 따라 2 일간 골프 퍼팅 연습을 하였다. 24시간 후 골프 퍼팅에 대한 파지검사와 연구대상자간의 경쟁 시합을 하였고 집단과 기간에 따라 퍼팅 점수, QE, 스윙시간, 후두엽의 알파파워를 산출하여 비교 분석하였다. [결과] 첫째, 세 집단 모두 사전보다 파지, 경쟁에서 퍼팅 점수가 높아진 것으로 나타났다. 특히 QE집단은 통제집단보다 높은 퍼팅 점수를 보이며 통계적으로 유의한 차이가 나타났다. 그러나 시각차단 집단은 두 집단과의 차이가 나타나지 않았다. 둘째, QE집단은 사전보다 파지, 경쟁에서 QE가 통계적으로 길어지는 것으로 나타났다. 그리고 통제집단은 사전보다 경쟁에서 QE가 다소 길어지는 경향을 보였다. 그러나 시각차단 집단은 기간에 따라 QE의 변화가 나타나지 않았다. 셋째, 세 집단 모두 기간이 지남에 따라 스윙시간이 유의하게 길어졌다. 넷째, 후두엽의 알파파워는 집단과 기간에 따라 유의한 차이가 나타나지 않았다. [결론] 시각차단 집단은 자세가 안정적이게 변함에 불구하고 QE는 길어지지 않았으나, 눈을 뜨고 연습한 QE집단은 QE가 길어졌고 통제집단도 QE가 길어지는 경향을 보였다. 그러므로 QE는 자세 조절 보다 시각에 의한 인지적 처리로 해석 할 수 있다. 마지막으로 본 연구를 통해 골프 퍼팅 초보자에게 QE연습이 보다 효율적인 연습방법임을 증명하였다.

Eye Tracking Glasses

Simulator

1 version available:

A state of the art and scoping review of embodied information behavior in shared, co-present extended reality experiences

We present a state of the art and scoping review of the literature to examine embodied information behaviors, as reflected in shared gaze interactions, within co-present extended reality experiences. Recent proliferation of consumer-grade head-mounted XR displays, situated at multiple points along the Reality-Virtuality Continuum, has increased their application in social, collaborative, and analytical scenarios that utilize data and information at multiple scales. Shared gaze represents a modality for synchronous interaction in these scenarios, yet there is a lack of understanding of the implementation of shared eye gaze within co-present extended reality contexts. We use gaze behaviors as a proxy to examine embodied information behaviors. This review examines the application of eye tracking technology to facilitate interaction in multiuser XR by sharing a user’s gaze, identifies salient themes within existing research since 2013 in this context, and identifies patterns within these themes relevant to embodied information behavior in XR. We review a corpus of 50 research papers that investigate the application of shared gaze and gaze tracking in XR generated using the SALSA framework and searches in multiple databases. The publications were reviewed for study characteristics, technology types, use scenarios, and task types. We construct a state-of-the field and highlight opportunities for innovation and challenges for future research directions.

Eye Tracking Glasses

Software

6 versions available

A study of the challenges of eye tracking systems and gaze interaction for individuals with motor disabilities

Eye tracking systems are crucial methods by which motor disabled people can interact with computers. Previous research in this field has identified various accessibility affecting eye tracking technologies and applications. However, there is limited research into first-hand user experiences among individuals with motor disabilities. This study aims to examine the actual challenges with eye tracking systems and the gaze interaction faced by motor disabled people. A survey was conducted among people with motor disabilities who used eye trackers for computer interactions. It reveals the current issues from their first-hand experiences in three areas: eye tracking program, gaze interaction, and accessible applications. A knowledge graph arising from the survey delineates the connections among the eye tracking usability issues. The survey’s results also indicate practical strategies for future improvements in eye trackers.

Eye Tracking Glasses

Software

5 versions available

Adaptive Driving Assistant Model (ADAM) for advising drivers of autonomous vehicles

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

Eye Tracking Glasses

Software

2 versions available

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Research on young and elderly drivers indicates a high crash risk amongst these drivers in comparison to other age groups of drivers. Young drivers have a greater propensity to adopt a risky driving style and behaviors associated with poor road safety. On the other hand, age-related declines can negatively impact the performance of older drivers on the road leading to crashes and risky maneuvers. Thus, autonomous vehicles have been suggested to improve the road safety and mobility of younger and older drivers. However, the difficulty of manually taking over control from semi-autonomous vehicles might vary in different driving conditions, particularly in those that are more challenging. Hence, the present study aims to examine the effect of road geometry and scenario, by investigating young, middle-aged and older drivers’ situation awareness (SA) and takeover performance when driving a semi-autonomous vehicle simulator on a straight versus a curved road on a highway and an urban non-highway road when engaged in a secondary distracting task. Due to the impact of COVID-19, data from only the young (n=24) and middle-aged (n=24) adults were collected and analyzed. Participants drove a Level 3 semi-autonomous simulator vehicle and performed a secondary non-driving related task in the distracted conditions. The results indicated that the participants had significantly longer hazard perception times on the curved roads and autopilot drives, but there was no significant effect of driver age and road type. Their Situation Awareness Global Assessment Technique (SAGAT) scores were higher in the highway scenarios, on the straight roads, and in the manual drive compared to the autopilot with distraction drive. Young drivers were also found to have significantly higher SAGAT scores than middle-aged drivers. While there was a significant interaction effect between road type and road geometry on takeover time, there was no significant main effect of road geometry, drive type and driver’s age. For the takeover quality metrics, road geometry and drive type had an effect on takeover performance. The resulting acceleration was higher for the straight road and in the autopilot drives, and the lane deviation was higher on the curved road and autopilot only drive compared to the autopilot with distraction drive. There was no significant main effect of road type and driver’s age on resulting acceleration and lane deviation. Overall, while there were age differences in some aspects of SA, young and middle-aged drivers did not differ in their takeover performance. The participants’ SA was impacted by the road type and geometry and their takeover quality varied according to the road geometry and drive type. The outcomes of this research will aid vehicle manufacturing companies that are developing Level 3 semi-autonomous vehicles with appropriately designing the lead time of the takeover request to meet the driving style and abilities of younger and middle-aged drivers. This will also help to improve road safety by reducing the crash rate of younger drivers.

Eye Tracking Glasses

Simulator

1 version available:

Analyzing Korean Reading Course and Designing Education-A Focus on Learner’s Eye-Tracking Analysis

The purpose of this study is to propose a model for designing reading education that can improve reading ability by finding and linguistically analyzing how the reading ability required for reading comprehension is presented at each learner level. In this study, the eye-tracking experiment was conducted divided into lexical and sentence levels which are the stage of comprehension corresponding to the sub-element of the reading comprehension, and the results are as follows. First, at the lexical level, it was confirmed that cognitive speed differs depending on the degree of preservation of the base form of the conjugations of the predicates with the same base form. Second, at the sentence level, we confirmed that the unstable reading-comprehension process appeared in the complex compared to the short sentence, even if the sentence length, vocabulary level, and vocabulary were similar and in the case of that the sentence has the same meaning. This paper suggests that the spelling, vocabulary, conjugation, grammar knowledge that correspond to the lower level should be included as the elements of education.

Eye Tracking Glasses

Software

1 version available:

Analyzing the influencing factors and workload variation of takeover behavior in semi-autonomous vehicles

There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload.

Eye Tracking Glasses

Simulator

13 versions available

Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance

Netherlands Railways has developed driver advisory systems (DAS) to provide the train driver with route context information and coasting advice in order to benefit punctuality and energy efficiency. However, the impact of these DAS on human factors aspects and safety performance is unclear. The current study assesses the impact of two DAS levels (route context information and coasting advice) on mental workload, attention allocation and safety performance, using eye tracking, a subjective mental workload rating scale (RSME) and simulator data. The overall findings suggest that the application of DAS levels has no negative impact on safety performance and attention allocation towards the trackside compared to a control condition with static timetable information. Furthermore, safety performance benefits significantly from DAS with route context information. DAS were originally developed to benefit punctuality and energy efficiency goals. This study implicates that DAS can also benefit safety performance. The current study found that DAS could decrease workload when the functionalities meet the requirements of the situation. The possible presence of mental underload and its effect on driving performance should be taken into consideration when implementing DAS. It is essential in the development of DAS that it meaningfully enriches the train driving task in stead of simply increasing mental workload.

Simulator

Software

7 versions available

Company

Publication Hub

Filter menu

Where to gaze during take-over: eye gaze strategy analysis of different situation awareness and hazard perception levels

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

A QE Study on Golf Putting

A state of the art and scoping review of embodied information behavior in shared, co-present extended reality experiences

A study of the challenges of eye tracking systems and gaze interaction for individuals with motor disabilities

Adaptive Driving Assistant Model (ADAM) for advising drivers of autonomous vehicles

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Analyzing Korean Reading Course and Designing Education-A Focus on Learner’s Eye-Tracking Analysis

Analyzing the influencing factors and workload variation of takeover behavior in semi-autonomous vehicles

Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance

Explore Cutting-Edge Research in Human Factors and Ergonomics