• Journal of IKEEE
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• v.21 no.4
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• pp.358-367
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• 2017

Advanced Driver Assistance Systems(ADAS), such as Front Collision Warning System (FCWS) are currently being developed. FCWS require high processing speed because it must operate in real time while driving. In addition, a low-power system is required to operate in an automobile embedded system. In this paper, FCWS is implemented in CPU-FPGA architecture in embedded system to enable real-time processing. The lane detection enabled the use of the Inverse Transform Perspective (IPM) and sliding window methods to operate at fast speed. To detect the vehicle, a Convolutional Neural Network (CNN) with high recognition rate and accelerated by parallel processing in FPGA is used. The proposed architecture was verified using Intel FPGA Cyclone V SoC(System on Chip) with ARM-Core A9 which operates in low power and on-board FPGA. The performance of FCWS in HD resolution is 44FPS, which is real time, and energy efficiency is about 3.33 times higher than that of high performance PC enviroment.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.91-101
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• 2003

This study modeled 4-lane highway in three-dimensional virtual reality in order to overcome difficulties of field experiment. and the research subject was placed in a driving simulator. We survey the driver's cognitive characteristics to the alignment changes in the three-dimensional virtual reality highway. Especially, maximizing the identity of driving movements and virtual scenery on the basis of the data obtained by dynamic analysis module. we minimized simulator sickness for the graphic module of driving simulator. And we carried out cognitive evaluation on the basis of adjective words extracted by dictionary and the opinion of specialist. In this study LISREL model was used to detect the causal relation between geometry and safety in cognitive side, and found that geometric change affects the safety of drivers by static and dynamic road safety model in three-dimensional combined alignments. As the result, for constructing safety road. we consider drivers' cognitive characteristics as human factors in road design, and we think that they are very important factors to improve road safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.273-286
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• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.469-472
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• 2022

With the recent development of intelligent transportation systems, various technologies applying deep learning technology are being used. To crackdown on illegal vehicles and criminal vehicles driving on the road, a vehicle type classification system capable of accurately determining the type of vehicle is required. This study proposes a vehicle type classification system optimized for mobile traffic control systems using YOLO(You Only Look Once). The system uses a one-stage object detection algorithm YOLOv5 to detect vehicles into six classes: passenger cars, subcompact, compact, and midsize vans, full-size vans, trucks, motorcycles, special vehicles, and construction machinery. About 5,000 pieces of domestic vehicle image data built by the Korea Institute of Science and Technology for the development of artificial intelligence technology were used as learning data. It proposes a lane designation control system that applies a vehicle type classification algorithm capable of recognizing both front and side angles with one camera.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.278-290
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• 2016

Maximum traffic flow rate is an important performance measure of operational status in transport networks, and has been considered as a key parameter for transportation operation since a bottleneck in congestion decreases maximum traffic flow rate. Although previous studies for traffic flow analysis have been widely conducted, a detection method for changes in dynamic traffic flow has been still veiled. This paper explores the dynamic traffic flow detection that can be utilized for various traffic operational strategies. Turning point analysis (TPA), as a statistical method, is applied to detect the changes in traffic flow rate. In TPA, Bayesian approach is employed and vehicle arrival is assumed to follow Poisson distribution. To examine the performance of the TPA method, traffic flow data from Jayuro urban expressway were obtained and applied. We propose a novel methodology to detect turning points of dynamic traffic flow in real time using TPA. The results showed that the turning points identified in real-time detected the changes in traffic flow rate. We expect that the proposed methodology has wide application in traffic operation systems such as ramp-metering and variable lane control.

• Journal of the Korean Society for Railway
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• v.19 no.3
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• pp.280-287
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• 2016

In railway tunnels, dust is generated when trains run due to the contact between the wheels and the rails. The generated dust is suspended due to the train-induced airflow, with some of it deposited due to gravitational sedimentation. In this study, the diurnal variation of the dust concentration was investigated in a railway tunnel. A single lane of a tunnel was selected in which to observe more easily the dust concentration due to the passing of a train. Four particle-measuring instruments were utilized to detect dust ranging from 5nm to $20{\mu}m$. To synchronize the train passing time at the measuring location, a three-dimensional ultrasonic anemometer and a video camera were used. It was found that the dust concentration was significantly increased from $50{\mu}g/m^3$ to $150{\mu}g/m^3$ due to the train. Particularly, the dust concentration was greatly increased to more than $250{\mu}g/m^3$ during the morning rush-hour times.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.235-245
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• 2008

Since rapidly increase of tunnel with increasing of expressway, the study on safety improvement of safety device at entrance of expressway tunnels is necessary. The existence of tunnel occurs more speed reduction than an upward slope by itself, the collision accident of tunnel entrance causes heavier damage than that of general accident on the road. So, many kinds of safety devices such as poly-ethylene barrier, guard-rail are placed on the road side. But these devices affect the drivers as an obstacle. Although there are various safety devices that are placed at tunnel entrance, this study is related to following 2-cases. One is that the poly-ethylene barrier is placed and the other is that a safety devices is not placed. The reason that these two cases are selected, is that poly-ethylene barrier is usually placed at many tunnel entrances and safety devices can affect the drivers as an obstacle. This study is related to the difference of right-hand side clearance between inside tunnel and outside tunnel, too. The average difference observed car speed and VDS(vehicle detect system) speed nearby the tunnel is analysed. Through the statistical analysis of the average difference, this study suggests an alternatives on safety improvement of safety devices at entrance of expressway tunnels. It is concluded that the small difference of right-hand side clearance is desirable to drivers when a poly-ethylene barrier is placed. And when the difference of right-hand side clearance is large, no safety devices is desirable, and when the difference of right-hand side clearance is small, poly-ethylene barrier should be placed to improve safety.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.181-190
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• 2022

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.