• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1551-1558
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• 2013

The purpose of this study is deriving proper plan which is improving functionality of three-way intersection in two-way road by using shoulder. Alternatives of this study were considered as installation of yield lane and application of TWLTLs (Two-Way Left-Turn Lanes). Case studies to utilize alternatives were limited to national and local roadways which is wider than 11 meters due to be required 3 lanes. Under various traffic conditions such as traffic volume of each direction and left-turn, alternatives were analyzed by simulation. As a results, application of TWLTLs was better than installation of yield lane in terms of improving rate (8.0% vs. 3.7%). Application of TWLTLs is supposed to better alternative, however enough driver education is required to improving safety because it is different with existing driving pattern and/or behaviors. Some limitations and future research agenda have also been discussed by on-site inspections.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.143-151
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• 2017

PURPOSES : The purpose of this study is to develop models of accidents occurring at circular intersections related to the time of day and night and driver gender, and to provide countermeasures for safer circular intersections. METHODS : Seventy intersections built before 2008 were surveyed for inclusion in the modeling. Traffic accident data from 2008 to 2014 were collected from the TAAS data set of the Road Traffic Authority. Sixteen variables explaining the accidents including geometry and traffic volume were selected from the literature and seven multiple linear regression models were developed using SPSS 20.0. RESULTS : First, the null hypotheses, that the number of traffic accidents are not related to driver gender or time of day, were rejected at a 5% level of significance. Second, seven statistically significant accident models with $R^2$ value of 0.643-0.890 were developed. Third, in daytime models by gender, when the right-turn-only lane was selected as the common variable, the number of lanes, presence of driveways and speed humps, diagrammatic exit destination sign, and total entering traffic volume were evaluated as specific variables. Finally, in nighttime models by gender, when the diagrammatic exit destination sign was selected as the common variable, total entering traffic volume, presence of right-turn-only lanes, number of circulatory road way lanes, and presence of splitter islands and driveways were identified as specific variables. CONCLUSIONS:This study developed seven accident models and analyzed the common and specific variables by time of day and gender. The results suggest approaches to providing countermeasures for safer circular intersections.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.277-284
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• 2014

The detection method of curves for the lanes which is powerful for the variation by utilizing the lane variation vector and cardinal spline on the inverse perspective transformation screen images which do not required the camera parameters are suggested in this paper. This method detects the lane area by setting the expected lane area in the s frame and next s+1 frame where the inverse perspective transformation and entire process of the lane filter are adapted, and expects the points of lane location in the next frames with the lane variation vector calculation from the detected lane areas. The scan area is set from the nextly expected lane position and new lane positions are detected within these areas, and the lane variation vectors are renewed with the detected lane position and the lanes are detected with application of cardinal spline for the control points inside the lane areas. The suggested method is a powerful method for curved lane detection, but it was adopted to the linear lanes too. It showed an excellent lane detection speed of about 20ms in processing a frame.

• Tunnel and Underground Space
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• v.15 no.6 s.59
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• pp.432-440
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• 2005

Baehuryeong tunnel connects Chuncheon with Hwacheon in Kangwon, Korea, This tunnel is a single tunnel with 5,057 m long and two bidirectional lanes which will be extended into low lanes in the future. The estimated construction period of Baehuryeong tunnel is approximately 55 months. This tunnel will become the longest bidirectional roadway tunnel in Korea. Compared to a twin tunnel, a bidirectional single tunnel has two major disadvantages with regard to the ventilation system and ease of escape during fire. For these reasons, a service tunnel and the transverse ventilation system are planned first time in Korea. In case of fire, the tunnel ventilation design aims to maintain a smoke free layer for passenger evacuation. The geology of Baehuryeong tunnel site is mainly composed of gneiss and granite. Baehuryeong fault is a mainly large scale fault which stands vertical and parallels with tunnel direction. The influenced zone of this fault is within 70 m. Baehuryeong tunnel was designed that it was separated with the distance of more than 100 m from Baehuryeong fault for its safety.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.9-16
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• 2017

An Algorithm for Lane Detection and Lane Departure Warning for a Vehicle Driving on Roads is proposed in This Paper. Using Images Obtained from On-board Cameras for Lane Detection has Some Difficulties, e.g. the Increase of Fault Detection Ratio Due to Symbols on Roads, Missing Yellow Lanes in the Tunnel due to a Similar Color Lighting, Missing Some Lanes in Rainy Days Due to Low Intensity of Illumination, and so on. The Proposed Algorithm has been developed Focusing on Solving These Problems. It also has an Additional Function to Determine How much the Vehicle is leaning to any Side between The Lanes and, If Necessary, to Give a Warning to a Driver. Experiments Using an Image Database Built by Collecting with Vehicle On-board Blackbox in Six Different Situations have been conducted for Validation of the Proposed Algorithm. The Experimental Results show a High Performance of the Proposed Algorithm with Overall 97% Detection Success Ratio.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.88-93
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• 2012

Lane detection methods using camera, which are part of the driver assistance system, have been developed due to the growth of the vehicle technologies. However, lane detection methods are often failed by varying road conditions such as rainy weather and degraded lanes. This paper proposes a method for lane detection which is robust in varying road condition. Lane candidates are extracted by intensity comparison and lane detection filter. Hough transform is applied to compute the lane pair using lane candidates which is straight line in image. Then, a curved lane is calculated by using B-Snake algorithm. Also, weighting value is computed using previous lane detection result to detect the lanes even in varying road conditions such as degraded/missed lanes. Experimental results proved that the proposed method can detect the lane even in challenging road conditions because of weighting process.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.797-807
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• 2008

This paper proposes a new algorithm of extracting forward vehicle areas using the acquired lanes and road area information on road images with complex background to improve the efficiency of the vehicle detection. In the first stage, lanes are detected by taking into account the connectivity among the edges which are determined from a method of chain code. Once the lanes proceeding to the same direction with the running vehicle are detected, neighborhood roadways are found from the width and vanishing point of the acquired roadway of the running vehicle. And finally, vehicle areas, where forward vehicles are located on the road area including the center and neighborhood roadways, are extracted. Therefore, the proposed scheme of extracting forward vehicle area improves the rate of vehicle detection on the road images with complex background, and is highly efficient because of detecting vehicles within the confines of the acquired vehicle area. The superiority of the proposed algorithm is verified from experiments of the vehicle detection on road images with complex background.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.203-208
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• 2011

Traffic demand is continuously increasing due to the development of urban areas in Korea. To cope with this, many efforts have been done including constructions of new roadways and increasing the number of lanes of the existing roadways. Those efforts, however, have been performed for only a short segment of target links having similar traffic characteristics. As a result, most urban cities experience bottle-neck phenomena which lead decreasing flow speed and increasing possibilities of accidents. Thus, this study aims to analyze problems of bottle-neck phenomena and effects of balancing number of lanes along the same corridor having similar traffic characteristics. For this, Route 32 passing the city of Daejeon in Korea has been selected as a case study, and a micro-simulation tool, VISSIM, has been adopted for the analysis. The results show that balanced number of lanes along the same corridor with similar traffic characteristics can increase flow speeds and enhance safety.