• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.35-43
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• 2004

In this paper, we proposed a vehicle recognition and electronic toll collection system with detection and classification of vehicle identification mark and emblem as well as recognition of vehicle license plate to unman toll fee collection system or incoming/outcoming vehicles to an institution. In the proposed algorithm, we first process pre-processing step such as noise reduction and thinning from the rear side input image of vehicle and detect vehicle mark, emblem and license plate region using intensity variation informations, template masking and labeling operation. And then, we classify the detected vehicle features regions into vehicle mark and emblem as well as recognize characters and numbers of vehicle license plate using hybrid and seven segment pattern vector. To show the efficiency of the proposed algorithm, we tested it on real vehicle images of implemented vehicle recognition system in highway toll gate and found that the proposed method shows good feature detection/classification performance regardless of irregular environment conditions as well as noise, size, and location of vehicles. And also, the proposed algorithm may be utilized for catching criminal vehicles, unmanned toll collection system, and unmanned checking incoming/outcoming vehicles to an institution.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.263-272
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• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.32-38
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• 2023

With the transition from internal combustion engine vehicles to eco-friendly cars, it has become essential to systematically construct an education system for electric vehicles based on high-voltage electric devices. In this study, we discussed the establishment of an educational system for electric vehicles based on high-voltage electric devices and proposed methods for qualifications after completing the education. To ensure systematic education, we presented a classification of learners according to their levels and job competencies. Additionally, we emphasized the importance of providing adequate practical training equipment for courses that require higher qualifications. Finally, to distinguish between the levels of completion of training and practical skills, we highlighted the necessity of implementing a system to certificates to individuals who have successfully completed the systematic training program.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• Resources Recycling
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• v.14 no.2
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• pp.3-9
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• 2005

A study on the air and gravity separation has been performed for the removal of chlorine containing materials from ASR of end-of-life vehicles. The gravity separation was also conducted on waste plastics collected from ASR. In this work, ASR were previously shredded to pass through 8 mm sieve prior to separation tests and the gravity separation of waste plastics was conducted for three different particle sizes. The two-stage air classification was conducted with the range of air flow rate of 9~20 M$^3$/hr at first stage and 25~34 M$^3$/hr at second stage, respectively. The fraction of overflow product was remarkably increased in the 2nd stage air classification because of high air flow rate while that of underflow product obtained from 1st stage air classification was found to be 62~66%. From the results of gravity separation on waste plastics, it was also found that the amount of the float product was much greater than sink product. It is believed that the gravity separation may be used very efficiently for the removal of calorine bearing materials from waste plastics.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.15-26
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• 2013

This paper proposes a classification method of parallel, vertical parking states and pillars for parking assist system using ultrasonic sensors. Since, in general parking space detection module, the compressed amplitude of ultrasonic data are received, the analysis of them is difficult. To solve these problems, in preprocessing state, symmetric transform and noise removal are performed. In feature extraction process, four features, standard deviation of distance, reconstructed peak, standard deviation of reconstructed signal and sum of width, are proposed. Gaussian fitting model is used to reconstruct saturated peak signal and discriminability of each feature is measured. To find the best combination among these features, multi-class SVM and subset generator are used for more accurate and robust classification. The proposed method shows 92 % classification rate and proves the applicability to parking space detection modules.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.877-889
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• 2006

This paper introduces a simple but effective method for recognizing vehicle models corresponding to each maker by information and images for moving vehicles. The proposed approach is implemented by combination of the breadth detection mechanism using the vehicle's pressure, exact height detection by a laser scanning, and license plate recognition for classifying specific vehicles. The implemented system is therefore capable of robust classification with real-time vehicle's moving images and established sensors. Simulation results using the proposed method on synthetic data as well as real world images demonstrate that proposed method can maintain an excellent recognition rate for moving vehicle models because of image acquisition by 2-D CCD and various image processing algorithms.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.61-69
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• 2020

As a sales volume of autonomous vehicle continually grows up, regulations on this new technology are being introduced around the world. For example, safety standards for the Level 3 automated driving system was promulgated in December 2019 by the Ministry of Land, Infrastructure and Transport of Korean government. In order to promote the development of autonomous vehicle technology and ensure its safety simultaneously, the regulations on the automated driving systems should be phased in to keep pace with technology progress and market expansion. However, according to SAE J3016, which is well known to classify the level of the autonomous vehicle technologies, the description for classification is rather abstract. Therefore it is necessary to describe the automated driving system in more detail in terms of the 'Level.' In this study, the functions and characteristics of automated driving system are carefully classified at each level based on the commentary in the Informal Working Group (IWG) of the UN WP29. In particular, regarding the Level 4, technical issues are characterized with respect to vehicle tasks, driver tasks, system performance and regulations. The important features of the autonomous vehicles to meet Level 4 are explored on the viewpoints of driver replacement, emergency response and connected driving performance.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.35-41
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• 2024

In order to verify autonomous driving scenarios and safety, a lot of driving and accident data is needed, so various organizations are conducting classification and analysis of traffic accident types. In this study, it was determined that accident recording devices such as EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving) would become an objective standard for analyzing the causes of autonomous vehicle accidents, and traffic accidents that occurred from 2015 to 2020 were analyzed. Using the database system of IGLAD (Initiative for the Global Harmonization of Accident Data), approximately 360 accident data of EDR-equipped vehicles were classified and their characteristics were analyzed by comparing them with accident types of ADAS (Advanced Driver Assistance System)-equipped vehicles. It will be used to develop autonomous vehicle accident investigation guidelines in the future.