• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.30-37
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• 2014

The Electronic Toll Collection System based on Free-Flow Multi-Lane is needed to solve some yearly problems such as traffic congestion, safety accident, maintenance by hold-up of one way hi-pass system. The hi-pass communication system is first-in, first-out method in one way environment so, it can't handle various running patterns such as vehicle platoon, switching lane and passing in multi-lane environment. In this thesis we compared and analyzed the communication system of foreign countries ETCS operating system and domestic hi-pass communication system, then studied communication system which can run in multiple-way accepting existing hi-pass OBU. And we formed the communication system of Free-Flow Multi-Lane environment as a plan using incoherent of IR antenna and coherent of RF antenna. The communication system in Free-Flow Multi-Lane environment can be used not alone in parking garages but in expressways of ETCS and toll roads from now on.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.69-75
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• 2022

In this paper, a method for predicting the speed for each lane from the link speed using an artificial neural network is presented to increase the accuracy of predicting the required time of a driving route. The time required for passing through a link is observed differently depending on the direction of going straight, turning right, or turning left at the intersection of the end of the link. Therefore, it is necessary to predict the speed according to the vehicle's traveling direction. Data required for learning and verification were constructed by refining the data measured at the Gongpyeong intersection of Gukchaebosang-ro in Daegu Metropolitan City and four adjacent intersections around it. Five neural network models were used. In addition, error analysis of the prediction was performed to select a neural network experimentally suitable for the research purpose. Experimental results showed that the error in the estimation of the time required for each lane decreased by 17.4% for the straight lane, 4.4% for the right-turn lane, and 3.9% for the left-turn lane. This experiment is the result of analyzing only one link. If the entire pathway is tested, the effect is expected to be greater.

• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.25-40
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• 1990

This study was undertaken to develop a traffic signal timing method for interconnected and semi-protected-left-turn intersections(the intersections which have left-turn signal but not exclusive left-turn lanes) on four-lane streets for energy saving and to computerize the method for the practical use. For this study, a probability model which could estimate the utilized time of the shared left-turn lane by through traffic during green period was developed based on field studies. The two left-turn treatments, leading and lagging left-turns, were tested for the intersections, and it can be concluded that the leading left-turn was more efficient for the normal urban streets on which through traffic is major traffic. Adopting the leading left-turn macro-models to estimate vehicular average delay and proportions of vehicles stopped at the intersections were developed. Using the two models as well as the idling fuel consumpution rate and the excess fuel consumption per stop-go speed change, a traffic signal timing method for the intersections for energy saving was developed and computerized. The method can be used for more than four-lane streets and for other measures of effectiveness such as minimum delay, minimum stop rates, etc.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4D
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• pp.305-311
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• 2012

With the rapid deployment of hipass$^{(R)}$, the congestion is inevitable due to the operation of the hipass lane system. Recently, SMART Highway project have developed a multi-lane mainline tolling system, called SMART Tolling system. To analyze the effectiveness of the system in terms of capacity, this study tries to estimate the capacity and its improvement of multi-lane tolling system based on current hipass$^{(R)}$ data. The methodology uses the saturation time headway. This follows three steps; 1) estimate the saturation time headway, using hipass$^{(R)}$ data, and capacity. 2) estimate two factors (the first one is dividing the one side lane width and lateral clearance factor ($f_w$) into two side one, the second one is dividing the capacity of hipass lane operating a circuit breaker into the capacity of hipass lane not operating, the last one is increasing factor of lane width). 3) calculate the capacity of multi-lane mainline tolling system. The results of method produced 2172~2187 veh/hour as smart tolling capacities, respectively. Those are higher about 370 veh/hour than the values from existing literature reviews. Additionally, saturation time headways were identified as lower by 0.5 seconds/veh than existing headways based on hi-pass$^{(R)}$ based one, which naturally implies the improvement in capacity. Some limitations and future research agenda have also been discussed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.3
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• pp.51-58
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• 2006

The purpose of this research presents a method to position and makes the structure for eco-corridors reasonably with collectable analysing results of various effects shown in mammals' road-kill at 429 points. Target animals of this research are Leopard cat, Siberian weasel, Raccoon dog, Korean hare, Eurasian red squirrel, Siberian chipmunk and Water deer. The results derived from the empirical analysis on the contents above are followed. First, according to the results as for Leopard cat road kill analysis, which is designated as Endangered Species Class II, the eco-corridor might be located at near village having stead food in order to decrease the frequencies of road-kill, because its road kill points were mainly collected at 4 lane hilly road with mountain-road-farm area geological type of. Second, because Siberian weasel's road kill was detected at 2 lane hilly road with mountain-road-stream geological type, the eco-corridor might be located at near a mill to decrease road-kill frequencies. Third, the road-kill frequency of Eurasian red squirrel can be reduced when the eco-corridor is located at the area across coniferous tree near 4 lane west sea freeway with mountain-road-mountain. Fourth, the road-kill of Raccoon dog can be reduced when the eco-corridor is located at 4 lane mountain road or hilly road with the geological type having farm land-road-mountain(stream). Fifth, Korean hare's road-kill can be reduced when the eco-corridor is located at grass land across ridge line of mountain, because wild rabbit road kill was happened at 4 lane mountain road or 2 lane mountain road(mountain-road-mountain). Sixth, As for Siberian chipmunk, the eco-corridor might be located at the side slope of mountain road at 2 lane mountain road under the speed of 60km/h with mountain-road-mountain. Seventh, For Water deer, the eco-corridor might be located at 4 lane hilly road with mountain-road-farm land. As for Common otter, Amur hedgehog, Yellow-throated marten, Weasel, it is difficult to specify the proper site of eco-corridor due to the lack of data. Eco-corridors for carnivores might be well located at 4 lane hilly road or 2 lane hilly road with mountain-road-farm land, and the track for herbivores might be well located as a overhead bridge on mountain-road-mountain type across mountains. In order to position eco-corridors for wildlife properly, we have to research animal's behavior with ecological background, and to consider the local uniqueness and regularly collect the empirical road-kill data in long term 3 to 5 year, which can be the foundation for the more suitable place of wild life eco-corridors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.235-240
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• 2015

Active Safety system is requested on the market of the medium and heavy duty commercial vehicle over 4.5ton beside the market of passenger car with advancement of the digital equipment proportionally. Unlike the passenger car, the mounting position of camera in case of the medium and heavy duty commercial vehicle is relatively high, it is disadvantaged conditions for lane recognition in contradiction to passenger car. In this work, we show the method of lane recognition through the Sobel edge, based on the spatial domain processing, Hough transform and color conversion correction. Also we suggest the low error method of front vehicles recognition in order to reduce the detection error through Haar-like, Adaboost, SVM and Template matching, etc., which are the object recognition methods by frontal camera vision. It is verified that the reliability over 98% on lane recognition is obtained through the vehicle test.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.73-83
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• 2019

In this paper, we propose the augmented reality(AR) based driving navigation based on robust lane detection method to dynamic environment changes. The proposed technique uses the detected lane position as a marker which is a key element for enhancing driving information. We propose Symmetrical Local Threshold(SLT) algorithm which is able to robustly detect lane to dynamic illumination environment change such as shadows. In addition, by using Morphology operation and Connected Component Analysis(CCA) algorithm, it is possible to minimize noises in the image, and Region Of Interest(ROI) is defined through region division using a straight line passing through several vanishing points We also propose the augmented reality aided visualization method for Interchange(IC) and driving navigation using reference point detection based on the detected lane coordinates inside and outside the ROI. Validation experiments were carried out to assess the accuracy and robustness of the proposed system in vairous environment changes. The average accuracy of the proposed system in daytime, nighttime, rainy day, and cloudy day is 79.3% on 4600 images. The results of the proposed system for AR based IC and driving navigation were also presented. We are hopeful that the proposed research will open a new discussion on AR based driving navigation platforms, and thus, that such efforts will enrich the autonomous vehicle services in the near future.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.25-31
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• 2019

As artificial intelligence technology has been developed rapidly, many researchers who are interested in next-generation vehicles have been studying on applying the artificial intelligence technology to advanced driver assistance systems (ADAS). In this paper, a method of applying deep learning algorithm to the lane departure warning system which is one of the main components of the ADAS was proposed. The performance of the proposed method was evaluated by taking a comparative experiments with the existing algorithm which is based on the line detection using image processing techniques. The experiments were carried out for two different driving situations with image databases for driving on a highway and on the urban streets. The experimental results showed that the proposed system has higher accuracy and precision than the existing method under both situations.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.