• Journal of IKEEE
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• v.16 no.4
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• pp.349-355
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• 2012

Information technology improves convenience, safety, and performance of automobiles. Recently, a lot of algorithms are studied to provide safety and environment information for driving, and lane detection algorithm is one of them. In this paper, we propose a lane detection algorithm that reduces the amount of calculation by reducing region of interest (ROI) after preprocessing. The proposed algorithm reduces the area of ROI a lot by determining the candidate regions near lane boundaries as V-ROI so that the amount of calculation is reduced. In addition, the amount of calculation can be maintained almost the same regardless of the resolutions of the input images by compressing the images since the lane detection algorithm does not require high resolution. The proposed algorithm is implemented using C++ and OpenCV library and is verified to work at 30 fps for realtime operation.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.152-159
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• 2005

The method of induction noise reduction can be classified by using passive control or active control method. However, the passive control method has a demerit to reduce the effect of noise reduction to low frequency (below) 500Hz) range and to be limited in a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used in LMS (Least-Mean-Square) algorithm because it can obtain the complex transfer function easily in real-time. Especially, Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm could not match if the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. So, in order to solve the problem in this study, the Moving Bandpass Filter(MBPF) was proposed and implemented. The ANC using MBPF for the reduction of the induction noise shows that more noise reduction as 4dB than without MBPF.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.494-502
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• 2011

Bimodal tram is designed to run on a dedicated path in automatic mode using a magnetic track system in order to realize a combination of the accessibility of a bus and the constant regularity of a railroad. This paper presents design and test results of the series hybrid propulsion system of the bimodal tram on both test track and public road, which uses CNG (Compressed Natural Gas) engine and Lithium polymer battery pack. This paper describes the real-time data measuring equipment for the series hybrid propulsion system of the bimodal tram. Using this measurement equipment, the performance of the prototype vehicle's driving on test track and public road was verified and the fuel consumption and the efficiency of CNG engine have been investigated.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.51-62
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• 2009

Variable message signs (VMS) are a useful tool for providing real-time traffic information to drivers. In particular, effective warning information provision leading to safer driving would be an important countermeasure to prevent traffic accidents. The purpose of this study was to identify users' preferences for traffic safety warning information formats. A variety of warning information scenarios using text and pictograms were devised and investigated for the purpose of selecting more effective methods to provide warning information. A portable variable message sign (PVMS) was used to evaluate users' preferences. The results of this study can be used for designing better warning information for the enhancement of traffic safety.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.19-24
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• 2019

The development of ICT technology has a huge impact on the existing closed CCTV security equipment market. With the importance of video data particularly highlighted in areas such as self-driving cars, unmanned vehicles and smart cities, various technologies using video are emerging. In this paper, we proposed a method to transmit videos and metadata as a part of smart city integration, and to solve the traffic, environment and security problems caused in urban life by utilizing the metadata instead of using CCTV videos for simple recording purposes, and reverse tunneling technique was designed and implemented as a method for accessing CCTV videos for vehicles from remote locations. Integrated management of CCTV videos and metadata for vehicles that have been used only for limited purposes in closed environments will enable efficient operation of integrated centers in real time required by smart cities, such as vehicle status check, road conditions and facility management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.75-82
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• 2021

The number of traffic accidents is about 230,000, and due to non-recurring congestion and high driving speed, the number of deaths per traffic accident on freeways is more than twice compared to other roads. Currently, traffic information is provided based on nodes and links using the centerline of the road, but it does not provide detailed speed information. Recently, installing sensors for vehicles to monitor obstacles and measure location is becoming common not only for autonomous vehicles but also for ordinary vehicles as well. The analysis using large-capacity location-based data from such sensors enables real time service according to processing speed. This study presents an mapping method for individual vehicle data analysis based on space. The processing speed of large-capacity data was increased by using method which applied a quaternary notation basis partition method that splits into two directions of longitude and latitude respectively. As the space partition was processed, the average speed was similar, but the speed standard deviation gradually decreased, and decrease range became smaller after 9th partition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.153-172
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• 2018

Overloading of Commercial vehicles have been an important area of transportation as one of the main causes of pavement damage, bridge collapse, severe traffic accident, etc. In this study, we analyzed the effects of overweight prevention by analyzing overweight driving patterns and using weight sensors. First, we analyzed relevant literatures of overweight and surveyed the commercial weight sensors. Then we chose the typical type of overweight vehicles based of overweight enforcement data analysis. MEMs inclinometer weight sensor were installed to 10 test vehicles and data was collected by weight sensors and gps in real time. As a result of gross vehicle weight and axle weight analysis, it was found weight sensor could decrease overweight rate. However, since the number of samples of test vehicles is insufficient to represent the whole commercial vehicle, further studies are deemed possible through the extension test.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.157-163
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• 2019

The LiDAR sensor, which is widely regarded as one of the most important sensors, has recently undergone active commercialization owing to the significant growth in the production of ADAS and autonomous vehicle components. The LiDAR sensor technology involves radiating a laser beam at a particular angle and acquiring a three-dimensional image by measuring the lapsed time of the laser beam that has returned after being reflected. The LiDAR sensor has been incorporated and utilized in various devices such as drones and robots. This study focuses on object detection and recognition by employing sensor fusion. Object detection and recognition can be executed as a single function by incorporating sensors capable of recognition, such as image sensors, optical sensors, and propagation sensors. However, a single sensor has limitations with respect to object detection and recognition, and such limitations can be overcome by employing multiple sensors. In this paper, the performance of an eight-channel scanning LiDAR was evaluated and an object detection algorithm based on it was implemented. Furthermore, object detection characteristics during daytime and nighttime in a real road environment were verified. Obtained experimental results corroborate that an excellent detection performance of 92.87% can be achieved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.29-36
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• 2019

Unmanned Autonomous Vehicle's driving road in the national defense includes not only paved roads, but also unpaved roads which have rough and unexpected changes. This Unmanned Autonomous Vehicles monitor and recon rugged or remote areas, and defend own position, they frequently encounter environments roads of various and unpredictable. Thus, they need additional learning to drive in this environment, we propose a Additional Learning Nearest Neighbor (ALNN) which is modified from Approximate Nearest Neighbor to allow for quick learning while avoiding the 'Forgetting' problem. In addition, since the Execution speed of the ALNN algorithm decreases as the learning data accumulates, we also propose a solution to this problem using GPU parallel processing based on OpenGL Shader Language. The ALNN based on GPU algorithm can be used in the field of national defense and other similar fields, which require frequent and quick application of additional learning in real-time without affecting the existing learning data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.813-826
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• 2022

AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.