• Journal of Korean Society of Transportation
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• v.1 no.1
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• pp.56-63
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• 1983

Intersection control has dual-purposes; increasing capacity and reducing delay. The primary concern of efficient intersection control under oversaturated condition as in Korea is to increase capacity. Prevailing intersection operation technique permits thru traffic to utilize left turn lane, because the intersection without left turn pocket has left turn signal interval. In this situation, it seems not to be valid to calculate capacity, delay, and signal timings by conventional methods. By critical lane technique, capacity increases as cycle length increases. However, when thru traffic utilize LT lane, the capacity varies according to LT volume, LT interval as well as cycle length, which implies that specific cycle length and LT interval exist to maximize capacity for given LT volume. The study is designed is designed to calculate utilization factors of LT lane for thru traffic and capacities, and identify signal timings to yield maximum capacity. The experimental design involved has 3 variables; 1)LT volumes at each approach(20-300 vph), 2)cycle lengths (60-220 sec), and 3)LT intervals(2.6-42 sec) for one scenario of isolated intersection crossing two 6-lanes streets. For LT volume of 50-150 vph, capacity calculated by using the utilization factor is about 25% higher than that by critical lane method. The range of optimum cycle length to yield maximum capapcity for LT volume less than 120 vph is 140-180 sec, and increases as LT volume increases. The optimum LT interval to yield maximum capacity is longer than the intrval necessary to accommodate LT volume at saturation flow rate.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.1-7
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• 2016

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.181-199
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• 2022

This study presented a methodology for evaluating the performance of the lane departure warning system was derived by calculating the relationship between the behavior information of the car and the location of the high-precision map using a high-precision map. The evaluation criteria of the mood and lane departure warning system for the installation of road markings in Korea were analyzed, a high-precision map was constructed to meet the evaluation criteria, and an evaluation system was constructed to verify the proposed methodology. Evaluation of lane departure warning systems using high-precision maps can be quantified and applied to various road environments through accurate location-based comparative analysis and reduced manual post-processing work time to confirm evaluation results.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.147-159
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• 2002

This paper proposes a method to recognize the noisiness of road images connected with the extraction of lane-related information in order to prevent the usage of erroneous information. The proposed method uses a fuzzy neural network(FNN) with the back-Propagation loaming algorithm. The U decides road images good or bad with respect to visibility of lane marks on road images. Most input parameters to the FNN are extracted from an edge distribution function(EDF), a function of edge histogram constructed by edge phase and norm. The shape of the EDF is deeply correlated to the visibility of lane marks of road image. Experimental results obtained by simulations with real images taken by various lighting and weather conditions show that the proposed method was quite successful, providing decision-making of noisiness with about 99%.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.3
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• pp.168-175
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• 1996

Except the collision avoidance performance related to the rapid lane change, the 4WS vehicle has better dynamic stability and handling performance than the conventional 2WS vehicle which has close relation with the driver's safety, a 4WS conrol method with the effect of steering wheel angular velocity is proposed based on the fact that the driver steers abruptly the steering wheel to avoid the collision. And the effects of the proposed 4WS control method are investigated on the dynamic stability and handling performance by using the ISO lane change test code.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.50-59
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• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.47-53
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• 2011

To enable the efficient operation of ITS, it is necessary to collect location data for vehicles on the road. In the case of futuristic transportation systems like ubiquitous transportation and smart highway, a method of data collection that is advanced enough to incorporate road lane recognition is required. To meet this requirement, technology based on radio frequency identification (RFID) has been researched. However, RFID may fail to yield accurate location information during high-speed driving because of the time required for communication between the tag and the reader. Moreover, installing tags across all roads necessarily incurs an enormous cost. One cost-saving alternative currently being researched is to utilize GNSS (global navigation satellite system) carrierbased location information where available. For lane recognition using GNSS, a precise digital map for determining vehicle position by lane is needed in addition to the carrier-based GNSS location data. A "precise digital map" is a map containing the location information of each road lane to enable lane recognition. At present, precise digital maps are being created for lane recognition experiments by measuring the lanes in the test area. However, such work is being carried out through comparison with vehicle driving information, without definitions being established for detailed performance specifications. Therefore, this study analyzes the performance requirements of a precise digital map capable of lane recognition based on the accuracy of GNSS location information and the accuracy of the precise digital map. To analyze the performance of the precise digital map, simulations are carried out. The results show that to have high performance of this system, we need under 0.5m accuracy of the precise digital map.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.126-136
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• 2014

In the Advanced Driver Assistance Systems (ADAS) of smart vehicle and Intelligent Transportation System (ITS) for to detect the boundary of lane is being studied a lot of Hough Transform. This method detects correctly recognition the lane. But recognition rate can fall due to detecting straight lines outside of the lane. In order to solve this problems, this paper proposed an algorithm to recognize the lane boundaries and the accumulator cells in Hough space. Based on proposed algorithm, we develop application for Android was developed by H/W verification. Users of smart phone devices could use lane detection and lane departure warning systems for driver's safety whenever and wherever. Software verification using the OpenCV showed efficiency recognition correct rate of 93.8% and hardware real-time verification for an application development in the Android phone showed recognition correct rate of 70%.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.158-164
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• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.