• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.9-21
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• 1998

This paper a new solution approach to moving obstacle avoidance problem of a mobile robot. A new concept avoidability measure (AVM) is defined to describe the state of a pair of a robot and an obstacle regarding the collision between them. As an AVM, virtual distance function (VDF), is derived as a function of the distance from the obstacle to the robot and outward speed of the obstacle relative to the robot. By keeping the virtual distance above some positive limit value, the robot avoids the obstacle. In terms of the VDF ,an artificial potential field is constructed to repel the robot away from the obstacle and to attract the robot toward a goal location. At every sampling time, the artificial potential field is updated and the force driving the robot is derived from the gradient of the artificial potential field. The suggested algorithm drives the robot to avoid moving obstacles in real time. Since the algorithm considers the mobility of the obstacle as well as the distance, it is effective for moving obstacle avoidance. Some simulation studies show the effectiveness of the proposed approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.248-251
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• 2003

This paper presents a distance perception model based around a moving camera, in the context of driving a self-guidance vehicle. Aligned images, by escape points, and acquired by a moving camera, present objects at different positions depending on its relative distance to camera. The objects that are farthest from the observer(the camera) gradually lose their alignment as the distance diminishes. With the current setup, this lack of alignment is noticeable up to a distance of 10 meters. In the paper, the results of real imagery tests are presented and discussed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.142-148
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• 2006

Considering the domestic traffic environment and the increase of traffic accidents, we have been asked to exactly analyze the main causes of accidents for the accident-experienced drivers to be rehabilitated. In this thesis we present the development process and results of a driving simulator using the IPDE method in the interest of safe driving and driving rehabilitation. Through this Driving simulation development the rehabilitated driver has the possibility of experiencing the real driving situation with the driving aptitude and examines the reasons of accidents. Through the examinations the driver has the chance to correct the deformities of driving by choosing the explanatory scenes, and through this process the driver is able to develop the capability to react in the real situation. However this driving simulation system is one of the best developed, depending on weather and road condition the braking distance may change. Therefore the fuzzy rule and neural network have been used in this thesis to solve previously mentioned problem. The simulation exactly calculated the road and weather conditions to adjust the breaking intensity.

• Journal of computational fluids engineering
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• v.19 no.2
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• pp.66-71
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• 2014

Aerodynamic design of a vehicle has very important meaning on the fuel economy, dynamic stability and the noise & vibration of a moving vehicle. In this study, the correlation of aerodynamic effect between two model vehicles moving inline on a road was studied with the basic SAE model vehicle. Drag and lift are two main physical forces acting on the vehicle and both of them directly effect on the fuel economy and driving stability of the vehicle. For the research, the distance between two vehicles is varied from 5m to 30m at the fixed vehicle speed, 100km/h and the side-wind was assumed to be zero. The main issue for this numerical research is on the understanding of the interaction forces; lift and drag between two vehicles formed inline. From the study, it was found that as the distance between two vehicles is closer, the drag force acting on both the front and rear vehicle decreases and the lift force has same trend for both vehicle. As the distance(D) is 5m, the drag of the front vehicle reduced 7.4% but 28.5% for the rear-side vehicle. As the distance is 30m, the drag of the rear vehicle is still reduced to 22% compared to the single driving.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2665-2674
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• 2012

This paper propose the method to recognize the lanes and distance between cars in real-time which detects dangerous situations and helps safe driving in the actual road environment. First of all, it extracts the area of interest corresponding to roads and cars from the road image photographed by using the forward-looking camera. Through the hough transform for the area of interest, this study detects linear components and also selects the lane and conducts filtering by calculating probability. And through the shadow threshold analysis of the cars in front within the area of interest, it extracts the objects of cars in front and calculates the distance from cars in front. According to the result of applying the suggested technology to recognize the lane and distance between cars to the road situation for testing, it showed over 95% recognition rate; thus, it has been proved that it can respond to safe driving.

• Convergence Security Journal
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• v.12 no.6
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• pp.31-37
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• 2012

Today, the core technology of an automobile is becoming to IT-based convergence system technology. To cope with many kinds of situations and provide the convenience for drivers, various IT technologies are being integrated into automobile system. In this paper, we propose an convergence system, which is called Augmented Driving System (ADS), to provide high safety and convenience of drivers based on image information processing. From imaging sensor, the image data is acquisited and processed to give distance from the front car, lane, and traffic sign panel by the proposed methods. Also, a converged interface technology with camera for gesture recognition and microphone for speech recognition is provided. Based on this kind of system technology, car accident will be decreased although drivers could not recognize the dangerous situations, since the system can recognize situation or user context to give attention to the front view. Through the experiments, the proposed methods achieved over 90% of recognition in terms of traffic sign detection, lane detection, and distance measure from the front car.

• Journal of the Korean Institute of Gas
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• v.15 no.4
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• pp.15-20
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• 2011

This study presents the behavior characteristics of buried three-layered pipeline subjected to pile driving loads corresponding to its cover depth. The analysis considered the driving energy caused by 7 tonf of ram weight and 1.2m of stroke with 20m distance from buried pipeline for all the analysis cases. A cover depth of pipe is varied 0.6m to 3.8m for this research. Vibration Velocity and stress are investigated at the center section in longitudinal direction. With same distance from pile, attenuation ratio of vibration velocity for increment of cover depth has shown an increasing trend. Also, Stress attenuation ratio of inner pipe is increased with cover depth.

• Journal of IKEEE
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• v.27 no.1
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• pp.1-11
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• 2023

Path driving tests are necessary for the development of self-driving cars or robots. These tests are being conducted in simulation as well as real environments. In particular, for development using reinforcement learning and deep learning, development through simulators is also being carried out when data of various environments are needed. To this end, it is necessary to utilize not only manually designed paths but also various randomly and automatically designed paths. This test site design can be used for actual construction and manufacturing. In this paper, we introduce a method for randomly generating a driving test path consisting of a combination of arcs and segments. This consists of a method of determining whether there is a collision by obtaining the distance between an arc and a line segment, and an algorithm that deletes part of the path and recreates an appropriate path if it is impossible to continue the path.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.305-311
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• 2016

Even though automakers have actively been conducting studies on autonomous navigation thanks to the development and application of wireless Internet technology, the traffic accident has been kept unsolved. The causes of the accident are drowsy driving, a mistake of a driver, environmental factors, and a wrong road structure; Driving manner and characteristics of a driver among the causes are significantly influential for the accident. In this paper, a study to measure characteristics of zigzag driving that can be seen before an occurrence of an accident regarding traffic accidents that can be incurred while driving manually or autonomously was conducted. While existing studies measured zigzag driving based on characteristics of the change of lateral angular velocity by imaging techniques or driving manner on the first and second lane, this study proceeded to measure zigzag driving by setting a lateral moving distance and a critical value range by utilizing the value of a sensor.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.172-185
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• 2021

As the era of autonomous driving approaches, it is expected to have a significant impact on our lives. When autonomous driving cars emerge, it is necessary to develop an index that can evaluate autonomous driving cars as it enhance the productive value of the car by reducing the burden on the driver. This study analyzed how the autonomous driving era affects commuting time and commuting time satisfaction among office goers using a car in Gyeonggi-do. First, a nonlinear relationship (V) was derived for the commuting time and commuting time satisfaction. Here, the factors affecting commuting time satisfaction were analyzed through a binomial logistic model, centered on the sample belonging to the nonlinear section (70 minutes or more for commuting time), which is likely to be affected by the autonomous driving era. The analysis results show that the variables affected by the autonomous driving era were health, sleeping hours, working hours, and leisure time. Since the emergence of autonomous driving cars is highly likely to improve the influencing variables, long-distance commuters are likely to feel higher commuting time satisfaction.