scholarly journals On the Difference Between Necessary and Unnecessary Glances Away From the Forward Roadway: An Occlusion Study on the Motorway

2019 ◽  
Vol 62 (7) ◽  
pp. 1117-1131 ◽  
Author(s):  
Katja Kircher ◽  
Tuomo Kujala ◽  
Christer Ahlström
Keyword(s):  
Visual Information ◽  
Situational Factors ◽  
Minimum Amount ◽  
Safe Driving ◽  
Detection Algorithms ◽  
Visual Occlusion ◽  
Visual Sampling ◽  
Naturalistic Driving ◽  
The Difference

Objective The present study strove to distinguish traffic-related glances away from the forward roadway from non-traffic-related glances while assessing the minimum amount of visual information intake necessary for safe driving in particular scenarios. Background Published gaze-based distraction detection algorithms and guidelines for distraction prevention essentially measure the time spent looking away from the forward roadway, without incorporating situation-based attentional requirements. Incorporating situation-based attentional requirements would entail an approach that not only considers the time spent looking elsewhere but also checks whether all necessary information has been sampled. Method We assess the visual sampling requirements for the forward view based on 25 experienced drivers’ self-paced visual occlusion in real motorway traffic, dependent on a combination of situational factors, and compare these with their corresponding glance behavior in baseline driving. Results Occlusion durations were on average 3 times longer than glances away from the forward roadway, and they varied substantially depending on particular maneuvers and on the proximity of other traffic, showing that interactions with nearby traffic increase perceived uncertainty. The frequency of glances away from the forward roadway was relatively stable across proximity levels and maneuvers, being very similar to what has been found in naturalistic driving. Conclusion Glances away from the forward roadway proved qualitatively different from occlusions in both their duration and when they occur. Our findings indicate that glancing away from the forward roadway for driving purposes is not the same as glancing away for other purposes, and that neither is necessarily equivalent to distraction.

scholarly journals A Review of Occlusion as a Tool to Assess Attentional Demand in Driving

2021 ◽  
pp. 001872082110109
Author(s):  
Tuomo Kujala ◽  
Katja Kircher ◽  
Christer Ahlström
Keyword(s):  
Visual Information ◽  
Attentional Demand ◽  
Detection Algorithms ◽  
Visual Occlusion ◽  
Vehicle System ◽  
Information Sampling ◽  
Occlusion Technique ◽  
Naturalistic Driving ◽  
Human Factors Research ◽  
Selection Of

Objective The aim of this review is to identify how visual occlusion contributes to our understanding of attentional demand and spare visual capacity in driving and the strengths and limitations of the method. Background The occlusion technique was developed by John W. Senders to evaluate the attentional demand of driving. Despite its utility, it has been used infrequently in driver attention/inattention research. Method Visual occlusion studies in driving published between 1967 and 2020 were reviewed. The focus was on original studies in which the forward visual field was intermittently occluded while the participant was driving. Results Occlusion studies have shown that attentional demand varies across situations and drivers and have indicated environmental, situational, and inter-individual factors behind the variability. The occlusion technique complements eye tracking in being able to indicate the temporal requirements for and redundancy in visual information sampling. The proper selection of occlusion settings depends on the target of the research. Conclusion Although there are a number of occlusion studies looking at various aspects of attentional demand, we are still only beginning to understand how these demands vary, interact, and covary in naturalistic driving. Application The findings of this review have methodological and theoretical implications for human factors research and for the development of distraction monitoring and in-vehicle system testing. Distraction detection algorithms and testing guidelines should consider the variability in drivers’ situational and individual spare visual capacity.

Situational Factors Competing for Attention

2010 ◽  
Vol 22 (1) ◽  
pp. 2-13 ◽  
Author(s):  
Weiyu Zhang ◽  
Se-Hoon Jeong ◽  
Martin Fishbein†
Keyword(s):  
Task Performance ◽  
Visual Information ◽  
Situational Factors ◽  
Sexual Content ◽  
Sexually Explicit ◽  
Explicit Content ◽  
Audio Information

This study investigates how multitasking interacts with levels of sexually explicit content to influence an individual’s ability to recognize TV content. A 2 (multitasking vs. nonmultitasking) by 3 (low, medium, and high sexual content) between-subjects experiment was conducted. The analyses revealed that multitasking not only impaired task performance, but also decreased TV recognition. An inverted-U relationship between degree of sexually explicit content and recognition of TV content was found, but only when subjects were multitasking. In addition, multitasking interfered with subjects’ ability to recognize audio information more than their ability to recognize visual information.

scholarly journals Survey of Hearing Aid Conditions for Hearing Impaired Persons While Driving

2020 ◽  
pp. 132-136
Author(s):  
Hiroshi Ikeda ◽  
Shigeyuki Minami
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Visual Information ◽  
Hearing Aid ◽  
Hearing Impaired ◽  
Safe Driving ◽  
Actual Usage ◽  
Hearing Ability ◽  
Acquired Hearing Loss ◽  
The Impact

Hearing impaired persons are required to drive with hearing aids to supplement their hearing ability, however, there has not been sufficient discussion regarding the impact of the use of a hearing aid on driving a vehicle. In order to investigate the actual usage and driving conditions of using hearing aids while driving a vehicle, this paper uses a questionnaire to survey (1) how easy it is to drive when wearing hearing aids, and (2) how often hearing aids are not worn while driving. Concerning the ease of driving when wearing a hearing aid, it was suggested that people with congenital hearing loss were more likely to rely on visual information, and those with acquired hearing loss continue to use their experience of hearing. When the level of disability is high, it is difficult to drive when using the hearing aid, and when the disability level is low, it is easier to drive. Regarding the frequency of driving without wearing hearing aids, about 60 % of respondents had such an experience. Those who often drive without hearing aids had experienced headaches due to noise from wearing hearing aids compared to those who wear hearing aids at all times. Hearing aids are necessary assistive devices for hearing impaired persons to obtain hearing information, and to provide a safe driving environment. Therefore, this paper addresses issues to maintain a comfortable driving environment while wearing a hearing aid.

scholarly journals Refixation Patterns of Mind-wandering during Real-world Scene Perception

2020 ◽  
Author(s):  
Han Zhang ◽  
Nicola C Anderson ◽  
Kevin Miller
Keyword(s):  
Eye Movements ◽  
Cognitive Processing ◽  
Real World ◽  
Visual Processing ◽  
Visual Information ◽  
Scene Perception ◽  
Mind Wandering ◽  
Quantification Analysis ◽  
The Difference ◽  
Thought Probes

Recent studies have shown that mind-wandering (MW) is associated with changes in eye movement parameters, but have not explored how MW affects the sequential pattern of eye movements involved in making sense of complex visual information. Eye movements naturally unfold over time and this process may reveal novel information about cognitive processing during MW. The current study used Recurrence Quantification Analysis (Anderson, Bischof, Laidlaw, Risko, & Kingstone, 2013) to describe the pattern of refixations (fixations directed to previously-inspected regions) during MW. Participants completed a real-world scene encoding task and responded to thought probes assessing intentional and unintentional MW. Both types of MW were associated with worse memory of the scenes. Importantly, RQA showed that scanpaths during unintentional MW were more repetitive than during on-task episodes, as indicated by a higher recurrence rate and more stereotypical fixation sequences. This increased repetitiveness suggests an adaptive response to processing failures through re-examining previous locations. Moreover, this increased repetitiveness contributed to fixations focusing on a smaller spatial scale of the stimuli. Finally, we were also able to validate several traditional measures: both intentional and unintentional MW were associated with fewer and longer fixations; Eye-blinking increased numerically during both types of MW but the difference was only significant for unintentional MW. Overall, the results advanced our understanding of how visual processing is affected during MW by highlighting the sequential aspect of eye movements.

scholarly journals High-Resolution Neural Network for Driver Visual Attention Prediction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2030 ◽  
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Visual Attention ◽  
Visual Information ◽  
Prediction Accuracy ◽  
Critical Role ◽  
Image Resolution ◽  
Feature Representation ◽  
Safe Driving ◽  
Feature Representations

Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver’s visual behavior in terms of computer vision to estimate the driver’s attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver’s attention locations.

scholarly journals Intercepting virtual balls approaching under different gravity conditions: evidence for spatial prediction

2017 ◽  
Vol 118 (4) ◽  
pp. 2421-2434 ◽  
Author(s):  
Marta Russo ◽  
Benedetta Cesqui ◽  
Barbara La Scaleia ◽  
Francesca Ceccarelli ◽  
Antonella Maselli ◽  
...  
Keyword(s):  
Visual Information ◽  
Internal Model ◽  
Spatial Location ◽  
Spatial Prediction ◽  
Wide Field ◽  
Flight Duration ◽  
Accelerated Motion ◽  
The Difference ◽  
Time Motor ◽  
Open Question

To accurately time motor responses when intercepting falling balls we rely on an internal model of gravity. However, whether and how such a model is also used to estimate the spatial location of interception is still an open question. Here we addressed this issue by asking 25 participants to intercept balls projected from a fixed location 6 m in front of them and approaching along trajectories with different arrival locations, flight durations, and gravity accelerations (0 g and 1 g). The trajectories were displayed in an immersive virtual reality system with a wide field of view. Participants intercepted approaching balls with a racket, and they were free to choose the time and place of interception. We found that participants often achieved a better performance with 1 g than 0 g balls. Moreover, the interception points were distributed along the direction of a 1 g path for both 1 g and 0 g balls. In the latter case, interceptions tended to cluster on the upper half of the racket, indicating that participants aimed at a lower position than the actual 0 g path. These results suggest that an internal model of gravity was probably used in predicting the interception locations. However, we found that the difference in performance between 1 g and 0 g balls was modulated by flight duration, the difference being larger for faster balls. In addition, the number of peaks in the hand speed profiles increased with flight duration, suggesting that visual information was used to adjust the motor response, correcting the prediction to some extent. NEW & NOTEWORTHY Here we show that an internal model of gravity plays a key role in predicting where to intercept a fast-moving target. Participants also assumed an accelerated motion when intercepting balls approaching in a virtual environment at constant velocity. We also show that the role of visual information in guiding interceptive movement increases when more time is available.

scholarly journals Verification and comparison of MIT-BIH arrhythmia database based on number of beats

2021 ◽  
Vol 11 (6) ◽  
pp. 4950
Author(s):  
Akram Jaddoa Khalaf ◽  
Samir Jasim Mohammed
Keyword(s):  
Signal Processing ◽  
Ecg Signal ◽  
Annotation File ◽  
Qrs Detection ◽  
Detection Algorithms ◽  
Matlab Program ◽  
Ecg Signal Processing ◽  
The Difference ◽  
Signal Processing Methods ◽  
Ecg Database

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

scholarly journals Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 585-594
Author(s):  
Shivangi Anthwal ◽  
Dinesh Ganotra
Keyword(s):  
Interpersonal Communication ◽  
Facial Expressions ◽  
Visual Information ◽  
Human Cognition ◽  
Support Vector ◽  
Dynamic Features ◽  
Difference Image ◽  
Neutral Face ◽  
The Difference ◽  
Emotional Face

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.

scholarly journals Experience as a Safety Factor in Driving; Methodological Considerations in a Sample of Bus Drivers

Safety ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 37 ◽  
Author(s):  
Anders af Wåhlberg ◽  
Lisa Dorn
Keyword(s):  
Environmental Risk ◽  
Meta Analysis ◽  
Bus Drivers ◽  
Safe Driving ◽  
Driving Experience ◽  
Method Effects ◽  
Within Subjects ◽  
The Difference ◽  
Exact Size ◽  
Methodological Considerations

Experience is generally seen as an important factor for safe driving, but the exact size and details of this effect has never been meta-analytically described, despite a fair number of published results. However, the available data is heterogeneous concerning the methods used, which could lead to very different results. Such method effects can be difficult to identify in meta-analysis, and a within-study comparison might yield more reliable results. To test for the difference in effects between some different analytical methods, analyses of data on bus driver experience and crash involvement from a British company were conducted. Effects of within- and between-subjects analysis, non-linearity of effects, and direct and induced exposure methods were compared. Furthermore, changes in the environmental risk were investigated. Between-subject designs yielded smaller effects as compared to within-subjects designs, while non-linearity was not found. The type of exposure control applied had a strong influence on effects, as did differences in overall environmental risk between years. Apparently, “the effect of driving experience” means different things depending upon how calculations have been undertaken, at least for bus drivers. A full meta-analysis, taking several effects of methodology into account, is needed before it can be said that the effect of driving experience on crash involvement is well understood.

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