Effect of Driver Age and Distance Guide Sign Format on Driver Attention Allocation and Performance

2018 ◽  
Vol 62 (1) ◽  
pp. 1903-1907
Author(s):  
Maryam Zahabi ◽  
Patricia Machado ◽  
Mei Lau ◽  
Yulin Deng ◽  
Carl Pankok ◽  
...  
Keyword(s):  
Visual Attention ◽  
Target Identification ◽  
Age Groups ◽  
Random Order ◽  
Attention Allocation ◽  
Detection Accuracy ◽  
Support Design ◽  
Elderly Drivers ◽  
And Performance ◽  
Design Guidance

Although several studies have assessed the effect of business logo sign format on driver visual attention and performance, some concern has been expressed that findings may not be generalizable to other signage configurations. We conducted a driving simulation study to assess the effect of distance guide sign format on visual attention allocation, target detection accuracy, and driving performance considering driver demographics. Results revealed distance guide sign format, including random or distance-ordered presentation of destinations, to have no impact on driver visual attention, target identification, and vehicle control. However, elderly drivers had difficulty in identifying targets when destinations were presented in random order. In addition, elderly drivers exhibited conservative responses (i.e., reduced off-road visual attention and greater speed reductions) as compared to other age groups when exposed to distance guide signs. Findings support design guidance for on-road signage to account for driver demographics.

scholarly journals Benchmarking and screening new building energy models in the Toronto context

2021 ◽  
Author(s):  
Jennifer Harmer
Keyword(s):  
Building Design ◽  
Building Energy ◽  
Feedback Mechanism ◽  
Building Energy Modeling ◽  
Support Design ◽  
Policy Makers ◽  
Building Models ◽  
Energy Modelling ◽  
And Performance ◽  
Design Guidance

Building energy modeling is a well-established field but there is a lack of research to support design guidance and energy benchmarking using simulated results. This study presents a methodology for collecting information about planned buildings in Toronto from uploaded building energy modelling files, to be used as a basis of comparison for future models. The methodology includes the development of an algorithm for automating the generation of baseline building models. Key building design and performance characteristics are identified for inclusion in a database of new buildings in Toronto, and a feedback mechanism, to provide design guidance through comparative analysis and program screening, is detailed. The resultant database can be used by individual building design teams, urban planners, or policy-makers, as they work together to reduce the greenhouse gas emissions in Toronto through increased energy efficiency in the built environment.

scholarly journals Benchmarking and screening new building energy models in the Toronto context

2021 ◽  
Author(s):  
Jennifer Harmer
Keyword(s):  
Building Design ◽  
Building Energy ◽  
Feedback Mechanism ◽  
Building Energy Modeling ◽  
Support Design ◽  
Policy Makers ◽  
Building Models ◽  
Energy Modelling ◽  
And Performance ◽  
Design Guidance

Building energy modeling is a well-established field but there is a lack of research to support design guidance and energy benchmarking using simulated results. This study presents a methodology for collecting information about planned buildings in Toronto from uploaded building energy modelling files, to be used as a basis of comparison for future models. The methodology includes the development of an algorithm for automating the generation of baseline building models. Key building design and performance characteristics are identified for inclusion in a database of new buildings in Toronto, and a feedback mechanism, to provide design guidance through comparative analysis and program screening, is detailed. The resultant database can be used by individual building design teams, urban planners, or policy-makers, as they work together to reduce the greenhouse gas emissions in Toronto through increased energy efficiency in the built environment.

scholarly journals Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

2021 ◽  
Vol 10 (6) ◽  
pp. 377
Author(s):  
Chiao-Ling Kuo ◽  
Ming-Hua Tsai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Detection Accuracy ◽  
Geospatial Information ◽  
Combination Rules ◽  
And Performance ◽  
Road Characteristics ◽  
Machine Readable ◽  
Background Image

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

Effect of Changing a Traffic Control Device Color on Driver Behavior and Perception across Different Age Groups

2021 ◽  
pp. 036119812110111
Author(s):  
Hatem Abou-Senna ◽  
Mohamed El-Agroudy ◽  
Mustapha Mouloua ◽  
Essam Radwan
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Driving Simulator ◽  
Age Groups ◽  
The United States ◽  
Time Of Day ◽  
General Purpose ◽  
Traffic Density ◽  
Driving Conditions ◽  
And Performance

The use of express lanes (ELs) in freeway traffic management has seen increasing popularity throughout the United States, particularly in Florida. These lanes aim at making the most efficient transportation system management and operations tool to provide a more reliable trip. An important component of ELs is the channelizing devices used to delineate the separation between the ELs and the general-purpose lane. With the upcoming changes to the FHWA Manual on Uniform Traffic Control Devices, this study provided an opportunity to recommend changes affecting safety and efficiency on a nationwide level. It was important to understand the impacts on driver perception and performance in response to the color of the EL delineators. It was also valuable to understand the differences between demographics in responding to delineator colors under different driving conditions. The driving simulator was used to test the responses of several demographic groups to changes in marker color and driving conditions. Furthermore, participants were tested for several factors relevant to driving performance including visual and subjective responses to the changes in colors and driving conditions. Impacts on driver perception were observed via eye-tracking technology with changes to time of day, visibility, traffic density, roadway surface type, and, crucially, color of the delineating devices. The analyses concluded that white was the optimal and most significant color for notice of delineators across the majority of subjective and performance measures, followed by yellow, with black being the least desirable.

Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

2021 ◽  
Vol 18 (2) ◽  
pp. 1-17
Author(s):  
Shannon P. Devlin ◽  
Jennifer K. Byham ◽  
Sara Lu Riggs
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Performance Measure ◽  
Task Demands ◽  
Unexpected Finding ◽  
Impact Performance ◽  
History Effects ◽  
And Performance ◽  
Workload History ◽  
Over Time

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

scholarly journals Visual Attention and Aging: an Age-Related Cognitive Model of Positive Adaptation

2014 ◽  
Vol 4 (3) ◽  
pp. 181-191
Author(s):  
José Manuel Rodríguez-Ferrer
Keyword(s):  
Young People ◽  
Visual Attention ◽  
Response Times ◽  
Age Groups ◽  
Cognitive Model ◽  
Covert Attention ◽  
Positive Adaptation ◽  
Healthy Elderly ◽  
Age Related ◽  
Healthy Young People

We have studied the effects of normal aging on visual attention. Have participated a group of 38 healthy elderly people with an average age of 67.8 years and a group of 39 healthy young people with average age of 19.2 years. In a first experiment of visual detection, response times were recorded, with and without covert attention, to the presentation of stimuli (0.5º in diameter grey circles) appearing in three eccentricities (2.15, 3.83 and 5.53° of visual field) and with three levels of contrast (6, 16 and 78%). In a second experiment of visual form discrimination circles and squares with the same features as in the previous experiment were presented, but in this case subjects only should respond to the emergence of the circles. In both age groups, the covert attention reduced response times. Compared to young people, the older group achieved better results in some aspects of attention tests and response times were reduced more in the stimuli of greater eccentricity. The data suggest that there is a mechanism of adaptation in aging, in which visual attention especially favors the perception of those stimuli more difficult to detec

scholarly journals Individual differences in working memory capacity predict visual attention allocation

2003 ◽  
Vol 10 (4) ◽  
pp. 884-889 ◽  
Author(s):  
M. Kathryn Bleckley ◽  
Francis T. Durso ◽  
Jerry M. Crutchfield ◽  
Randall W. Engle ◽  
Maya M. Khanna
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Visual Attention ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Allocation

scholarly journals Visual attention strategies for target object detection

2021 ◽  
Author(s):  
◽  
Ibrahim Mohammad Hussain Rahman
Keyword(s):  
Visual Attention ◽  
Object Detection ◽  
Target Object ◽  
Detection Accuracy ◽  
Estimation Model ◽  
Top Down ◽  
Bottom Up ◽  
Feature Map ◽  
Low Level ◽  
Visual Tasks

<p>The human visual attention system (HVA) encompasses a set of interconnected neurological modules that are responsible for analyzing visual stimuli by attending to those regions that are salient. Two contrasting biological mechanisms exist in the HVA systems; bottom-up, data-driven attention and top-down, task-driven attention. The former is mostly responsible for low-level instinctive behaviors, while the latter is responsible for performing complex visual tasks such as target object detection.  Very few computational models have been proposed to model top-down attention, mainly due to three reasons. The first is that the functionality of top-down process involves many influential factors. The second reason is that there is a diversity in top-down responses from task to task. Finally, many biological aspects of the top-down process are not well understood yet.  For the above reasons, it is difficult to come up with a generalized top-down model that could be applied to all high level visual tasks. Instead, this thesis addresses some outstanding issues in modelling top-down attention for one particular task, target object detection. Target object detection is an essential step for analyzing images to further perform complex visual tasks. Target object detection has not been investigated thoroughly when modelling top-down saliency and hence, constitutes the may domain application for this thesis.  The thesis will investigate methods to model top-down attention through various high-level data acquired from images. Furthermore, the thesis will investigate different strategies to dynamically combine bottom-up and top-down processes to improve the detection accuracy, as well as the computational efficiency of the existing and new visual attention models. The following techniques and approaches are proposed to address the outstanding issues in modelling top-down saliency:  1. A top-down saliency model that weights low-level attentional features through contextual knowledge of a scene. The proposed model assigns weights to features of a novel image by extracting a contextual descriptor of the image. The contextual descriptor plays the role of tuning the weighting of low-level features to maximize detection accuracy. By incorporating context into the feature weighting mechanism we improve the quality of the assigned weights to these features.  2. Two modules of target features combined with contextual weighting to improve detection accuracy of the target object. In this proposed model, two sets of attentional feature weights are learned, one through context and the other through target features. When both sources of knowledge are used to model top-down attention, a drastic increase in detection accuracy is achieved in images with complex backgrounds and a variety of target objects.  3. A top-down and bottom-up attention combination model based on feature interaction. This model provides a dynamic way for combining both processes by formulating the problem as feature selection. The feature selection exploits the interaction between these features, yielding a robust set of features that would maximize both the detection accuracy and the overall efficiency of the system.  4. A feature map quality score estimation model that is able to accurately predict the detection accuracy score of any previously novel feature map without the need of groundtruth data. The model extracts various local, global, geometrical and statistical characteristic features from a feature map. These characteristics guide a regression model to estimate the quality of a novel map.  5. A dynamic feature integration framework for combining bottom-up and top-down saliencies at runtime. If the estimation model is able to predict the quality score of any novel feature map accurately, then it is possible to perform dynamic feature map integration based on the estimated value. We propose two frameworks for feature map integration using the estimation model. The proposed integration framework achieves higher human fixation prediction accuracy with minimum number of feature maps than that achieved by combining all feature maps.  The proposed works in this thesis provide new directions in modelling top-down saliency for target object detection. In addition, dynamic approaches for top-down and bottom-up combination show considerable improvements over existing approaches in both efficiency and accuracy.</p>

Kinaesthesis: Its Measurement, Training and Relationship to Motor Control

1983 ◽  
Vol 35 (2) ◽  
pp. 411-421 ◽  
Author(s):  
J. I. Laszlo ◽  
P. J. Bairstow
Keyword(s):  
Motor Control ◽  
Individual Differences ◽  
Motor Skills ◽  
Age Groups ◽  
Large Individual ◽  
Motor Behaviour ◽  
Perceptual Training ◽  
Developmental Trends ◽  
And Performance ◽  
Improved Accuracy

This paper reviews studies which demonstrate the importance of kinaesthesis in the acquisition and performance of motor skills. A method of measuring kinaesthetic sensitivity in children and adults (recently developed) is briefly described. Developmental trends in kinaesthetic perception are discussed and large individual differences found within age groups. It was shown that kinaesthetically undeveloped children can be trained to perceive and memorize kinaesthetic information with greatly improved accuracy. Furthermore perceptual training facilitates the performance of a drawing skill. On the basis of these results an argument is made for the importance of kinaesthesis in skilled motor behaviour.

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