• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1693-1700
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• 2012

Recently, the warning system to aid drivers for safe driving is being developed. The system estimates the distance between the driver's car and the car before it and informs him of safety distance. In this paper, we designed and implemented the collision warning system which detects the car in front on the actual road situation and measures the distance between the cars in order to detect the risk situation for collision and inform the driver of the risk of collision. First of all, using the forward-looking camera, it extracts the interest area corresponding to the road and the cars from the image photographed from the road. From the interest area, it extracts the object of the car in front through the analysis on the critical value of the shadow of the car in front and then alerts the driver about the risk of collision by calculating the distance from the car in front. Based on the results of detecting driving cars and measuring the distance between cars, the collision warning system was designed and realized. According to the result of applying it in the actual road situation and testing it, it showed very high accuracy; thus, it has been verified that it can cope with safe driving.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.125-131
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• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.376-380
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• 2015

This paper proposes new recognition method of road lanes misty day. The method enables autonomous-driving of cars and the safety of the drivers while driving with bad visibility in foggy roads. The proposed method, firstly, determines whether the foggy or not according to pixel number distributions and starting point of the fog period from input images. If it is foggy then the median filter's size of the Retinex algorithm is set to 1000 or more and it performs histogram equalization and normalization. The computer simulation results show that the proposed method can recognize better long distances and fine detection than earlier methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.89-97
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• 2013

Recently, economic and social damages are globally increased due to the heavy snowfall caused by global warming. To reduce the damages of sudden regional heavy snow in roads, suitable countermeasures should be established based on the accurate detection of snowfall intensity for each roadway segment. In this paper, we deal with snowfall intensity detecting algorithm in the road area from CCD Imagery. First, we determine the MLZ (MotionLess Zone), which does not contain lane markings and moving cars, in the image space. Next, snow streaks trespassing the MLZ are extracted with Canny operator and proposed algorithm. Also, the concept of SII (Snow Intensity Index), which is the number of snow streaks during one minute in the MLZ, is defined. Finally, the effectiveness of proposed algorithm is proved by visually comparing the imagery and SII value obtained during 69 minutes. In consequence, we figured out that the integration of SII is significantly related to an actual amount of snowfall.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.183-193
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• 2019

This study developed a shockwave detection and prediction of their extinction point method based on continuous wavelet transform using trajectory data from probe vehicles equipped with automotive sensors.. To analyze the effectiveness of the proposed method, this paper proposed two measures which are a distance error between the extinction points of the predictor and an time-location error of the extinction points. The proposed concept was proved using the micro simulation based experiment with three exogenous variables of traffic volume, lane-close duration, market penetration of probe vehicles. The analysis results show that the proposed method is capable of detecting the traffic shockwaves as well as predicting their extinction point, and also that the accuracy of the proposed method is highly dependent on the rate of the probe vehicles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.948-956
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• 2014

Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.12
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• pp.1-7
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• 2004

We have developed a 24GHz side-looking vehicle detection radar. A 24GHz front-end module and a novel signal processing algorithm have been developed for speed measurement and size classification of vehicles in multiple lanes. The system has a fixed antenna and FMCW processing module. This paper presents the background theory of operation and shows some measured data using the algorithm. The data shows that measured velocity of the passing vehicle is within the accuracy of 95% in single lane and the velocity of the vehicles in two lanes is within the accuracy of 90% by using variable threshold estimation. The classification of vehicle size as small, medium and large has been measured with 89% accuracy.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.535-538
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• 2002

Machine vision algorithms, which are composed of noise elimination algorithm, crack detection and mapping algorithm, and path planning algorithm, are required for sealing crack networks effectively and automation of crack sealing.. Noise elimination algorithm is the first step so that computer take cognizance of cracks effectively. Noises should be removed because common road includes a lot of noises(mark of oil, tire, traffic lane, and sealed crack) that make it difficult the computer to acknowledge cracks accurately. The objective of this paper is to propose noise elimination algorithm, prove the efficiency of the algorithm through coding. The result of the coding is represented in this paper as well.

• Journal of Auto-vehicle Safety Association
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• v.5 no.1
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• pp.44-49
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• 2013

This paper presents a lateral collision risk index between an ego vehicle and a rear-side vehicle. The lateral collision risk is designed to represent a lateral collision risk and provide the appropriate threshold value of activation of the lateral collision management system such as the Blind Spot Detection(BSD). The lateral collision risk index is designed using the Time to Line Crossing(TLC) and the longitudinal collision index at the predicted TLC. TLC and the longitudinal collision index are calculated with the signals from the exterior sensor such as the radar equipped on the rear-side of a vehicle and a vision sensor which detects the distance and time to the lane departure. For the robust situation assessment, the perception of driving environment determining whether the road is straighten or curved should be determined. The relative motion estimation method has been proposed with the road information via the integrated estimator using the environment sensors and vehicle sensor. A lateral collision risk index was composed with the estimated relative motion considering the relative yaw angle. The performance of the proposed lateral collision risk index is investigated via computer simulations conducted using the vehicle dynamics software CARSIM and Matlab/Simulink.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.