• International Journal of Highway Engineering
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• v.15 no.6
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• pp.125-133
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• 2013

PURPOSES : The purpose of this study is to propose delay-minimizing operation methodology of a signalized intersection based upon optimization of lane-uses on approaching lanes for an intersection. METHODS : For the optimization model of lane-uses, a set of constraints are set up to ensure feasibility and safety of the lane-uses, traffic flow, and signal settings. Minimization of demand to saturation flow ratio of a dual-ring signal control system is introduced to the objective function for delay minimization and effective signal operation. Using the optimized lane-uses, signal timings are optimized by delay-based model of TRANSYT-7F. RESULTS : It was found that the proposed objective function is great relation with delay time for an intersection. From the experimental results, the method was approved to be effective in reducing delay time. Especially, cases for two left-turn lanes reduced greater delays than those for a left turn lane. It is noticed that the cases for different traffic volume by approach reduced greater delays than those for the same traffic volume by approach. CONCLUSIONS : It was concluded that the objective function is proper for lane-uses optimizing model and the operation method is effective in reducing delay time for signalized intersections.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.403-403
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• 2000

Generally estimation of driving direction uses the way which uses lane detection and vanishing point in autonomous-driving system. Especially we use Sub-window for decreasing Process time when we detect lane, but fixed sub-window can not detect lane because of some factors in road image. So we suggest algorithm using one-dimension line scan method to detect an exact position of lane.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1693-1703
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• 2003

This paper presents a new local obstacle avoidance method for indoor mobile robots. The method uses a new directional approach called the Lane Method. The Lane Method is combined with a velocity space method i.e., the Curvature-Velocity Method to form the Lane-Curvature Method (LCM). The Lane Method divides the work area into lanes, and then chooses the best lane to follow to optimize travel along a desired goal heading. A local heading is then calculated for entering and following the best lane, and CVM uses this local heading to determine the optimal translational and rotational velocities, considering some physical limitations and environmental constraint. By combining both the directional and velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the physical limitations of the robot motion into account.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.195-204
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• 2018

Lane keeping assist and departure prevention system are the key functions of ADAS. In this paper, we propose lane detection method which uses Gaussian function based RANSAC. The proposed method consists mainly of IPM (inverse perspective mapping), Canny edge detector, and Gaussian function based RANSAC (Random Sample Consensus). The RANSAC uses Gaussian function to extract the parameters of straight or curved lane. The proposed RANSAC is different from the conventional one, in the following two aspects. One is the selection of sample with different probability depending on the distance between sample and camera. Another is the inlier sample score that assigns higher weights to samples near to camera. Through simulations, we show that the proposed method can achieve good performance in various of environments.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.193-200
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• 2003

This paper proposes a novel algorithm to recognize the curve of a structured road. The proposed algorithm uses an LCF (Lane Curve Function) obtained by the transformation of a parabolic function defined on world coordinate into image coordinate. Unlike other existing methods, the algorithm needs no transformation between world coordinate and image coordinate owing to the LCF. In order to search for an LCF describing the lane best, the differential comparison between the slope of an assumed LCF and the phase angle of edge pixels in the LROI (Lane Region Of Interest) constructed by the LCF is implemented. As finding the true LCF, the lane curve is determined. The proposed method is proved to be efficient through various kinds of images, providing the reliable curve direction and the valid curvature compared to the real road.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.178-185
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• 1999

Recently, Collision Warning System is developed to improve vehicle safety. This system chiefly uses radar. But the detected vehicle from radar must be decide whether it is the vehicle in the same lane of my vehicle or not. Therefore, Vision System is needed to detect traffic lane. As a preparative step, this study presents the development of algorithm to recognize traffic lane curve direction. That is, the Neural Network that can recognize traffic lane curve direction is constructed by using the information of short distance, middle distance, and decline of traffic lane. For this procedure, the relation between used information and traffic lane curve direction must be analyzed. As the result of application to sampled 2,000 frames, the rate of success is over 90%.t text here.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.313-320
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• 1999

The Lane-Curvature Method(LCM) presented in this paper is a new local obstacle avoidance method for indoor mobile robots. The method combines Curvature-Velocith Method(CVM) with a new directional method called the Lane Method. The Lane Method divides the environment into lanes taking the information on obstacles and desired heading of the robot into account ; then it chooses the best lane to follow to optimize travel along a desired heading. A local heading is then calculated for entering and following the best lane, and CVM uses this heading to determine the optimal translational and rotational velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the dynamics of the robot Xavier, show the efficiency of the proposed method.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.