• Journal of the Korean Society of Safety
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• v.24 no.4
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• pp.28-33
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• 2009

This study examined the mounting status of cigar lighter receptacles for vehicles and analyzed the tracking phenomenon that occurs when foreign material entered a cigar lighter receptacle to obtain data for the analysis of accident investigation. Regardless of the vehicle's output, cigar lighter receptacles are mounted in a vehicle horizontally, vertically, or at tilting or inclined angle. The tilting type cigar lighter receptacle is much easier to use but current leakage resulting from foreign materials (coffee, beverages, water, etc.) falling into the cigar lighter receptacle may cause a fire to start. This study used a vehicle battery (DC 12V) as a power supply for the tracking test and configured its circuit in the same way as that of an electrical device in a vehicle. The tracking phenomenon that occurred in the standby mode of the vehicle exhibited a fine flame and an irregular occurrence of smoke. While this tracking phenomenon was occurring, the leakage current and the reaching distance of the flame were measured to be approximately 930mA and $20{\sim}50cm$, respectively. It is thought that the resultant flame may ignite toluene, dust, cigarettes, etc. It was observed that as the tracking progressed, the internal metal socket melted and a hole was created, the surface of which was also severely carbonized. In addition, the electrical resistance of the carbonized conductive path was measured to be approximately $30{\Omega}$. It is thought that this much resistance may cause local heating when leakage current flows and could ignite any nearby flammable material.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1496-1504
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• 2004

In this paper, we propose an algorithm for initializing a target vehicle detection, tracking the vehicle and estimating the distance from it on the stereo images acquired from a forward-looking stereo camera mounted on a road driving vehicle. The process of vehicle detection extracts road region using lane recognition and searches vehicle feature from road region. The distance of tracking vehicle is estimated by TSS correlogram matching from stereo Images. Through the simulation, this paper shows that the proposed method segments, matches and tracks vehicles robustly from image sequences obtained by moving stereo camera.

• International Journal of Contents
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• v.7 no.4
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• pp.19-23
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• 2011

This paper proposes a segmentation method for overlapped vehicles based on analysis of the vehicle location and the spatiotemporal association information. This method can be used in an intelligent transport system. In the proposed method, occlusion is detected by analyzing the association information based on a vehicle's location in continuous images, and occlusion segmentation is carried out by using the vehicle information prior to occlusion. In addition, the size variations of the vehicle to which association tracking is applied can be anticipated by learning the variations according to the overlapped vehicles' movements. To assess the performance of the suggested method, image data collected from CCTVs recording traffic information is used, and average success rate of occlusion segmentation is 96.9%.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.1
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• pp.124-130
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• 2001

This paper presents a methodology on automation of a trailer type vehicle which consists of two parts: a tractor and a trailer. Backward moving and parking control is very important to automate this type of vehicle. It is difficult to control the motion such a trailer vehicle whose dynamics in non-holonomic. Therefore, in this paper, the modeling and parameter estimation of the system using a RCGA(real-coded genetic algorithm) is proposed and a backward path tracking control algorithm is then obtained. The simulation results verify the effectiveness of the proposed method.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.71-80
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• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.124-127
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• 2000

This paper describes the algorithm for more fast tracking by compensation of the staring angle of antenna to receive the satellite signal changed by the mobile vehicle direction. The staring angle is compensated by signal processing from the electronic compass which is called VECTOR2X, and a left-right tracking method. Especially, when mobile vehicle is turning with high speed, it is observed a result which has more fast tracking time by using angle tracking technique compensated by electronic compass than one by only left right tracking method.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.363-374
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• 2012

This paper studies the problem of tracking a re-entry vehicle (RV) in order to predict its impact point on the ground. Re-entry target dynamics combined with super-high speed has a complex non-linearity due to ballistic coefficient variations. However, it is difficult to construct a database for the ballistic coefficient of a unknown vehicle for a wide range of variations, thus the reliability of target tracking performance cannot be guaranteed if accurate ballistic coefficient estimation is not achieved. Various techniques for ballistic coefficient estimation have been previously proposed, but limitations exist for the estimation of non-linear parts accurately without obtaining prior information. In this paper we propose the ballistic coefficient ${\beta}$ model-based interacting multiple model-extended Kalman filter (${\beta}$-IMM-EKF) for precise tracking of an RV. To evaluate the performance, other ballistic coefficient model based filters, which are gamma augmented filter, gamma bootstrapped filter were compared and assessed with the proposed ${\beta}$-IMM-EKF for precise tracking of an RV.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.130-139
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• 2011

In this paper, vehicle tracking algorithm using hue transformation in HIS color model is proposed. the proposed algorithm is installed on the road of the two horizontal virtual data sampling lines. The difference images are detected between the frame and the frame, respectively and also detected in the vehicle by using the hue color distribution to determine identity and lane changes. To examine the effectiveness of proposed algorithm, identification and velocity measurement for driving vehicle are evaluated. this evaluated results is shown by hue data of vehicle passing of two virtual data sample lines, and the velocity measurement for driving vehicle is less than 0.4% comparing with existing vehicle speed meter system.