• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.34-36
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• 2009

This paper proposes an effective lane detection algorithm using hugh transform and lane model. The proposed lane detection algorithm includes two major components, i.e., lane marks segmentation and an exact lane extraction using a novel postprocessing technique. The first step is to segment lane marks from background images using HSV color model. Then, a novel postprocessing is used to detect an exact lane using Hugh transform and lane models(linear and curved lane models). The postprocessing consists of three parts, i.e, thinning process, Hugh Transform and filtering process. We divide input image into three regions of interests(ROIs). Based on lane curve function(LCF), we can detect an exact lane from various extracted lane lines. The lane models(linear and curved lane mode]) are used in order to judge whether each lane segment is fit or not in each ROIs. Experimental results show that the proposed scheme is very effective in lane detection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.329-334
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• 2009

The various behaviors of vehicular traffic flow are generated through both car-following and lane-changing behaviors of vehicles. Especially lane-usage varies by lane-changing behaviors. In the area of microscopic vehicle simulation, a lane-changing model connected to a car-following model parallel is essential to generate both various traffic flows relationships and laneusages. In Korea, some studies on car-following models have been reported, but few studies for lane-changing models stay in the beginning stage. In this paper, a two-lane changing model for the simulation modeling of large freeway network is introduced. The lane-changing model is developed based on CA (Cellular Automata) model. The developed model is parallel combined with an existing CA car-following model and tested on a closed link system. The results of simulation show that the developed model generates the various behaviors of lane usage, which existing CA lane-changing models could not generate. The presented model is expected to be used for the simulation of more various freeway traffic flows.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.655-670
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• 2014

In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.144-152
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• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.130-136
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• 2015

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.222-224
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• 2009

This paper presents an algorithm that extracts road lane's specific information by using HSI color information and performance enhancement of lane detection base on vision processing of drive assist. As a preprocessing for high speed lane detection, 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 and it also increases reliabilities by deleting edges those are misrecognized. Road lane is extracted 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 noise can be removed by using saturation and brightness of HSI color model. Also it searches for the road lane's color information and extracts characteristics. The real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.109-118
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• 2009

The conventional lane changing model has been developed without acceleration or deceleration of vehicles at target lane. Thus, existing lane changing models have limitation to apply in real world. In this paper, lane changing model considered acceleration or deceleration, and calculated the safety distance between subject vehicle and adjacent vehicles for lane changing as well. Simulation was conducted to verify the validity and the efficiency of the developed lane changing model in this paper. Several scenarios were carefully examined by safety distance between subject vehicle and adjacent vehicles. In the result, it was verified that if gap between subject vehicle and adjacent vehicles is larger than safety distance, lane changing behavior between subject vehicle and adjacent vehicles avoids collision. The suggested lane changing model may be applied at the future driver assistance system and advanced safety vehicle.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.131-138
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• 2012

PURPOSES : Two-lane highways have one lane in each direction, and lane changing and passing maneuvers take place in the opposing lane depending on the availability of passing sight distance. 2001 Korea Highway Capacity Manual (KHCM) is classified into two classes of two-lane highways (Type I, II), and average travel speed and time-delayed rate are used as measures of effectiveness (MOEs). However, since existing two-lane highways have both uninterrupted and interrupted traffic flow-system elements, a variety of free-flow speeds exhibits in two-lane highways. In addition, it is necessary to check if the linear-relationship between volumes and time-delayed rate is appropriate. Then, this study is to reestablish the relationship between average travel speed, time-delayed rate, and flow. METHODS : TWOPAS model was selected to conduct this study, and the free-flow speeds of passenger cars and the percentage of following vehicles observed in two-lane highways were applied to the model as the input. The revised relationships were developed from the computer simulation. RESULTS : In the revised average travel speed vs. flow relationship, the free-flow speed of 90km/h and 70km/h were added. It shows that the relationship between time delayed-rate and flow appeared to be appropriate with the log-function form and that there was no difference in time-delayed rate between the free flow speeds. In addition to revise the relationships, the speed prediction model and the time-delayed rate prediction model were also developed. CONCLUSIONS : The revised relationships between average travel speed, time-delayed rate, and flow would be useful in estimating the Level of Service(LOS) of a two-lane highway.

• International Journal of Highway Engineering
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• v.17 no.5
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• pp.93-103
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• 2015

PURPOSES : The purpose of this study is to present a linear programing optimization model for the design of lane-based lane-uses and signal timings for an isolated intersection. METHODS: For the optimization model, a set of constraints for lane-uses and signal settings are identified to ensure feasibility and safety of traffic flow. Three types of objective functions are introduced for optimizing lane-uses and signal operation, including 1) flow ratio minimization of a dual-ring signal control system, 2) cycle length minimization, and 3) capacity maximization. RESULTS : The three types of model were evaluated in terms of minimizing delay time. From the experimental results, the flow ratio minimization model proved to be more effective in reducing delay time than cycle length minimization and capacity maximization models and provided reasonable cycle lengths located between those of other two models. CONCLUSIONS : It was concluded that the flow ratio minimization objective function is the proper one to implement for lane-uses and signal settings optimization to reduce delay time for signalized intersections.