• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.735-739
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• 2010

Driving workload is increasing according to developing new in-vehicle devices and introducing driving information systems. In this research using a driving simulator, EFRP (Eye Fixation Related Potential) was measured for evaluating driving attention and distraction while tasking cognitive workload, n-back tasks. The result of EFRP was compared with driver behaviors. Results suggest that EFRP is able to use for a method of evaluating driving workload, however, the analysis of driver behavior is difficult to find driving attention and distraction in the case of free flow of traffic situation.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.62-69
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• 2013

Visual demands associated with in-vehicle display usage and text messaging distract a driver's visual attention from the roadway. To minimize eyes-off-the-road demands, voice interaction systems are widely introduced. Under cognitively distracted condition, however, awareness of the operating environment will be degraded although the driver remains oriented to the roadway. It is also know that the risk of inattentive driving varies with age, thus systematic analysis of driving risks is required for the older drivers. This paper aims to understand the age-related driving performance degradation and visual attention changes under auditory cognitive demand which consists of three graded levels of cognitive complexity. In this study, two groups, aged 25-35 and 60-69, engaged in a delayed auditory recall task, so called N-back task, while driving a simulated highway. Comparisons of younger and older drivers' driving performance including mean speed, speed variability and standard deviation of lane position, and gaze dispersion changes, which consist of x-axis and y-axis of visual attention, were conducted. As a result, it was observed that gaze dispersion decreased with each level of demand, demonstrating that these indices can correctly rank order cognitive workload. Moreover, gaze dispersion change patterns were quite consistent in younger and older age groups. Effects were also observed on driving performance measures, but they were subtle, nonlinear, and did not effectively differentiate the levels of cognitive workload.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.116-129
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• 2023

Recently, interest in autonomous driving has drawn attention to autonomous cooperative driving, which considers the development of driving technology of autonomous vehicles and the development of infrastructure that constitutes a driving environment. According to the concept of autonomous cooperative driving, This study analyzes the new infrastructure for autonomous driving that can complement the information of existing precise road maps and adding HD map layer as the new infrastructure. The new infrastructure for autonomous driving presented two types of improved facilities and one type of sensor only facility. Analysis of HD maps shows that information such as junction points rarely changes, but it is expected that infrastructure for autonomous driving can be added to convey the meaning of paying attention to obstacles that may arise at the junction. In this way, the new infrastructure for autonomous driving needs to support the roles of guidance, instruction, and attention that existing road facilities.

• Therapeutic Science for Rehabilitation
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• v.9 no.3
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• pp.53-61
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• 2020

Objective : The purpose of this study is to evaluate the reaction time and divided attention related to driving in elderly subjects using the driving ability assessment tool. By analyzing differences in average score according to age group, we also aim to, provide data for identifying the risk of driving in elderly people. Methods : A total of 70 elderly subjects, who participated in a driving evaluation program for people over 65 years of aged and who live in W city, Gangwon-do from August to December 2019, were included in the study. After the driving questionnaire was completed, the mobile driving assessment tool was explained, and then the patients carried out the reaction time and the divided attention task. Collected data were analyzed using the statistical program SPSS 25.0, and the significance level was set to 0.1. Results : The reaction time of the younger-old was 0.717 s, while that of the older-old was 0.843 s, this difference was statistically significant (p=.084). The response time for the task of divided attention was 0.669 s in the younger-old and 0.695 s for the older-old. In this case, there was no statistically significant difference between the two groups (p=.901). Conclusion : Using the mobile driving ability assessment tool, it was possible to evaluate the reaction rate and divided attention of elderly while driving.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.421-431
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• 2022

The purpose of this research is to determine the point of fixation of the driver during the process of driving. Based on the results of this research, the driving instructor can make a judgement on what the trainee stare on the most. Traffic accidents have become a serious concern in modern society. Especially, the traffic accidents among unskilled and elderly drivers are at issue. A driver should put attention on the vehicles around, traffic signs, passersby, passengers, road situation and its dashboard. An eye-tracking-based application was developed to analyze the driver's gaze behavior. It is a prototype for real-time eye tracking for monitoring the point of interest of drivers in driving practice. In this study, the driver's attention was measured by capturing the movement of the eyes in real road driving conditions using these tools. As a result, dwelling duration time, entry time and the average of fixation of the eye gaze are leading parameters that could help us prove the idea of this study.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.166-172
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• 2018

The distraction of the driver's attention causes as much traffic accidents as drowsiness driving. Yet though there have been many studies on drowsiness driving, research on distraction driving is insufficient. In this paper, we divide distraction of attention into visual distraction and cognitive distraction and analyze the EEG of subjects while viewing images of distracting situations. The results show that more information is received and processed when distractions occur. It is confirmed that the probability of accident increases when the driver receives overwhelming amount of information that he or she cannot concentrate on driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.178-188
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• 2020

This paper proposes a lane recognition CNN network using split-attention network as a backbone to extract feature. Split-attention is a method of assigning weight to each channel of a feature map in the CNN feature extraction process; it can reliably extract the features of an image during the rapidly changing driving environment of a vehicle. The proposed deep neural networks in this paper were trained and evaluated using the Tusimple data set. The change in performance according to the number of layers of the backbone network was compared and analyzed. A result comparable to the latest research was obtained with an accuracy of up to 96.26, and FN showed the best result. Therefore, even in the driving environment of an actual vehicle, stable lane recognition is possible without misrecognition using the model proposed in this study.