• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.756-760
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• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.31-40
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• 2023

Given recent accidents involving autonomous vehicles, driver monitoring technology related to the transition of control in autonomous vehicles is gaining prominence. Driver status monitoring systems recognize the driver's level of alertness and identify possible impairments in the driving ability owing to conditions including drowsiness and distraction. In autonomous vehicles, predictive factors for the transition to manual driving should also be included. During traditional human driving, monitoring the driver's status is relatively straightforward owing to the consistency of crucial cues, such as the driver's location, head orientation, gaze direction, and hand placement. However, monitoring becomes more challenging during autonomous driving because of the absence of direct manual control and the driver's engagement in other activities, which may obscure the accurate assessment of the driver's readiness to intervene. Hence, safety-ensuring technology must be balanced with user experience in autonomous driving. We explore relevant global and domestic regulations, the new car assessment program, and related standards to extract requirements for driver status monitoring. This kind of monitoring can both enhance the autonomous driving performance and contribute to the overall safety of autonomous vehicles on the road.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.124-135
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• 2012

Purpose : The objective of this study was to investigate the differences of hippocampal volume and shape as well as the functional change between long life experienced taxi drivers and controls of Korean population. Materials and Methods: Three-dimensional T1-weighted images and blood oxygen level dependent functional MRI(fMRI) were obtained from 8 subjects, consisting of 4 experienced (20-30 years) taxi drivers and 4 age-matched controls. The hippocampal volume and shape were analyzed with three-dimensional T1-weighted images. In addition, neuronal activities of brain were analyzed using a blood oxygen level dependent fMRI between the two groups. Results: The hippocampal volume showed no statistically significant difference between the two groups (p > 0.05). The left hippocampi of the taxi drivers were slightly elongated with larger head and tail portions than those of the controls (p < 0.05, uncorrected). For the functional MRI, fusiform gyrus was specifically activated in taxi drivers, compared with the control group. Conclusion: The structural and functional changes of taxi driver's hippocampus indicate the functional differentiation as a result of occupational dependence on spatial navigation. In other words, the continuous usage of spatial navigation performance may diminish degeneration of hippocampus and the related brain regions.