• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.18-34
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• 2021

Pedestrian deaths in Korea due to traffic accidents are 40 percent of the fatalities in traffic accidents, which is about twice the average of OECD member countries. To reduce severe pedestrian accidents, it is necessary to apply the accident reduction measures to high-risk drivers (novice, elderly, and commercial vehicle drivers) who are more likely to cause traffic accidents than general drivers. Therefore, this study analyzed the effect of safe driving education on high-risk drivers' behavior. Here, the safe driving education is chosen as the measure to reduce traffic accidents. As part of the study, sudden pedestrian crossing situations were implemented in the driving simulator, and the vehicle trajectory data were collected to compare the driving behavior before and after the education. Most surrogate safety measures showed no improvement in the driving behavior of novice and elderly drivers, and the effect of safe driving education was found to be significant only in the group of commercial vehicle drivers. The results implied that additional measures such as pedestrian safety infrastructure and driver assistance systems, apart from the safe driving education, may be needed for novice and elderly drivers to reduce pedestrian accidents caused by them. With the findings mentioned above, this study is expected to provide a foundation to establish a plan to reduce pedestrian accidents caused by high-risk drivers.

• Journal of the Ergonomics Society of Korea
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• v.34 no.3
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• pp.247-264
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• 2015

Objective: The purpose of this study is to determine the priority of information presentation and the effective menu type to be placed in the center of a gauge cluster display for commercial trucks and to present a set of ergonomic designs for the gauge cluster display. Background: An effective ergonomic design is specifically needed for the development of the gauge cluster display for the commercial trucks, because more diverse and heavier information is delivered to truck drivers, compared to the information to passenger car drivers. Method: First, all the information that must be shown on the commercial truck display was collected. Then, the severity, frequency of use, and display design parameters were evaluated for those information by commercial truck drivers. Next, an analysis on the information attributes and the heuristic evaluation utilizing the display design principles were carried out. According to the results, a design alternative of the main screen to be displayed was constructed by priority. A comparative analysis between the alternative and existing main screens was also conducted to see the efficacy of the designs. Lastly, we conducted an experiment for the selection of menu type. The experiment was conducted using the driving simulator with an eye-tracking device. The independent variables were four types of the menu reflecting the commercial truck characteristics such as grid type, icon type, list type, and flow type. We measured preference, total execution time, the total duration of fixation on the gauge cluster area, and the total number of fixation on the gauge cluster area as dependent variables. Results: Four types of driver convenience information and six types of driver assistance information were selected as the information to be placed primarily on the main screen of the gauge cluster. The Grid type was the most effective among the menu types. Conclusion: In this study, the information that appears on the main screen of the display, the division of the display and the design of the menu type for commercial truck drivers were suggested. Application: This study is expected to be utilized as guidelines on the ergonomic design of a gauge cluster display for commercial trucks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.152-164
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• 2015

A rapid development of semiconductors, sensors and mobile network technologies has enable that the embedded device includes high sensitivity sensors, wireless communication modules and a video processing module for vehicular environment, and many researchers have been actively studying the smart car technology combined on the high performance embedded devices. The vehicle is increased as the development of society, and the risk of accidents is increasing gradually. Thus, the advanced driver assistance system providing the vehicular status and the surrounding environment of the vehicle to the driver using various sensor data is actively studied. In this paper, we design and implement the smart vehicular camera device providing the V2X communication and gathering environment information. And we studied the method to create the metadata from a received video data and sensor data using video analysis algorithm. In addition, we invent S-ROI, D-ROI methods that set a region of interest in a video frame to improve calculation performance. We performed the performance evaluation for two ROI methods. As the result, we confirmed the video processing speed that S-ROI is 3.0 times and D-ROI is 4.8 times better than a full frame analysis.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.23-31
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• 2010

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.113-120
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• 2014

In this paper, we examine Asymmetric Multi-Processing Environment to provide LDW service. Asymmetric Multi-Processing Environment consists of high-speed MCU to support rapid image processing and low-speed MCU for controlling with other ECU at the control domain. Also we designed rapid image process application and LDW application Software Component(SW-C) according to the development process rule of AUTOSAR. To communicate between two MCUs, timer based polling based IPC was designed. Also to communicate with other ECUs(Electronic Control Units), we designed CAN messages to provide alarm information and receiving CAN message to catch the Turn signal. We confirm the possibility of the various ADAS development using an Asymmetric Multi-Processing Environment and AUTOSAR platform. We also expect providing ISO 26262 functional safety.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1088-1095
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• 2009

In this paper, we describe the reorientation method of distorted road sign by using projection transformation for improving recognition rate of road sign. RSR (Road Sign Recognition) is one of the most important topics for implementing driver assistance in intelligent transportation systems using pattern recognition and vision technology. The RS (Road Sign) includes direction of road or place name, and intersection for obtaining the road information. We acquire input images from mounted camera on vehicle. However, the road signs are often appeared with rotation, skew, and distortion by perspective camera. In order to obtain the correct road sign overcoming these problems, projection transformation is used to transform from 4 points of image coordinate to 4 points of world coordinate. The 4 vertices points are obtained using the trajectory as the distance from the mass center to the boundary of the object. Then, the candidate areas of road sign are transformed from distorted image by using homography transformation matrix. Internal information of reoriented road signs is segmented with arrow and the corresponding indicated place name. Arrow area is the largest labeled one. Also, the number of group of place names equals to that of arrow heads. Characters of the road sign are segmented by using vertical and horizontal histograms, and each character is recognized by using SAD (Sum of Absolute Difference). From the experiments, the proposed method has shown the higher recognition results than the image without reorientation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.68-80
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• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.40-49
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• 2007

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be 'distracted' causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.21-29
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• 2017

For the efficient and stable operation of autonomous vehicles or advanced driver assistance systems being actively studied nowadays, it is important to determine the positions of the vehicle accurately and economically. A satellite based navigation system is mainly used for positioning, but it has a limitation in signal blockage areas. To overcome this limitation, sensor fusion methods including additional sensors such as an inertial navigation system have been mainly proposed but the high sensor cost has been a problem. In this work, we develop a vehicle position estimation algorithm using in-vehicle sensors and a low-cost imaging sensor without any expensive additional sensor. We determine the vehicle positions using the velocity and yaw-rate of a car from the in-vehicle sensors and the position and attitude of the camera based on the single photo resection process. For the evaluation, we built a prototype system, acquired test data using the system, and estimated the trajectory. The proposed algorithm shows the accuracy of about 40% higher than an in-vehicle sensor only method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.639-647
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• 2012

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.