• Title/Summary/Keyword: Straight lane

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Model-based Curved Lane Detection using Geometric Relation between Camera and Road Plane (카메라와 도로평면의 기하관계를 이용한 모델 기반 곡선 차선 검출)

  • Jang, Ho-Jin;Baek, Seung-Hae;Park, Soon-Yong
    • 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.

Vehicles' CO2 Emissions by Intersection Types (교차로 형태에 따른 차량 당 탄소가스 배출량 비교)

  • Kim, Da-Ye;Oh, Heung-Un
    • International Journal of Highway Engineering
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    • v.15 no.5
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    • pp.123-133
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    • 2013
  • PURPOSES : The present paper is to compare vehicles' $CO_2$ emissions in roundabouts and signalized intersections. METHODS : The present paper uses the SIDRA software with variables of traffic and road conditions. RESULTS : The results of the study are as follows : First, when entering traffic volumes are more than 1600pcph, vehicle's $CO_2$ emissions in roundabouts are lower than those of signalized intersections regardless of the left turn ratio. Second, When entering traffic volumes are more than 2800pcph, vehicles's $CO_2$ emissions in 2-lane approaches are lower than those of 1-lane approaches in signalized intersection. Third, when entering traffic volumes are more than 1600pcph, vehicle's $CO_2$ emissions of CASE B are lowest. (CASE B is the condition with one exclusive left-turn lane and one exclusive straight lane and one shared straight lane with right-turn.) Also, CASE A is the condition that vehicle's $CO_2$ emissions in roundabouts are lower than those of signalized intersections between 1600pcph and 3600pcph. (CASE A is the condition with one exclusive left-turn lane and one shared straight lane with right-turn.) But, when entering traffic volumes are more than 4000pcph, vehicle's $CO_2$ emissions in signalized intersections is lower than those of roundabouts. CONCLUSIONS : It may be concluded that vehicle's $CO_2$ emissions on roundabouts are much lower than those of signalized intersections, especially, when entering traffics volumes are between 1600pcph and 3600pcph in 1-lane or 2-lane approaches.

Realtime Robust Curved Lane Detection Algorithm using Gaussian Mixture Model (가우시안 혼합모델을 이용한 강인한 실시간 곡선차선 검출 알고리즘)

  • Jang, Chanhee;Lee, Sunju;Choi, Changbeom;Kim, Young-Keun
    • 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.

Developing the Traffic Accident Models of Arterial Link Sections by Driving Type (운전 유형에 따른 가로구간 사고모형 개발)

  • Kim, Kyung-Hwan;Park, Byung-Ho
    • Journal of the Korean Society of Safety
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    • v.25 no.6
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    • pp.197-202
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    • 2010
  • This study deals with the accident models of arterial link sections by driving type. The objectives is to develop models by driving type using the accident data of 24 arterial links in Cheong-ju. In pursuing the above, this study gives particular emphasis to modeling such the accidents as the straight, lane change and others. The main results analyzed are as follows. First, the number of accidents is analyzed to account for about 59% in straight, 31% in lane change and 10% in others. Second, the number of left-turn lane as common variables, and the ADT, number of pedestrian crossings, connecting roads and link length as specific variables are selected in developing models(number of accident and EPDO). Third, 8 models which are all statistically significant are developed. Finally, RMSE of the driving type models was analyzed to be better than that of dummy variable.

Vision Sensing for the Ego-Lane Detection of a Vehicle (자동차의 자기 주행차선 검출을 위한 시각 센싱)

  • Kim, Dong-Uk;Do, Yongtae
    • Journal of Sensor Science and Technology
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    • v.27 no.2
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    • pp.137-141
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    • 2018
  • Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

Predicting lane speeds from link speeds by using neural networks

  • Pyun, Dong hyun;Pyo, Changwoo
    • 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.

A Study for Driving Mechanism Evaluation of the Lane Keeping Assistance System (차선유지지원장치 작동 메커니즘 평가에 관한 연구)

  • Chung, Seung-Hwan;Kim, Jeong-Min;Kwon, Seong-Jin;Lee, Bong-Hyun
    • Journal of Auto-vehicle Safety Association
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    • v.5 no.1
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    • pp.69-74
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    • 2013
  • LKAS(Lane Keeping Assistance System) main function is to support the driver in keeping the vehicle within the current lane. Therefore, this system is able to reduce the driver workload with assisting the driver during driving. In this paper, we presented on study for test procedures and evaluation methods of the LKAS. The vehicle test conducted on straight road, left curve, right curve and four different types of lane under various vehicle speeds. This study proposed the LKAS system test procedures and methods that we are able to identify LKAS driving mechanism and performance.

EVALUATION OF FOUR-WHEEL-STEERING SYSTEM FROM THE VIEWPOINT OF LANE-KEEPING CONTROL

  • Raksincharoensak, P.;Mouri, H.I;Nagai, M.I
    • International Journal of Automotive Technology
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    • v.5 no.2
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    • pp.69-76
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    • 2004
  • This paper evaluates the effectiveness of four-wheel-steering system from the viewpoint of lane-keeping control theory. In this paper, the lane-keeping control system is designed on the basis of the four-wheel-steering automobiles whose desired steering response is realized with the application of model matching control. Two types of desired steering responses are presented in this paper. One is zero-sideslip response, the other one is steering response which realizes zero-phase-delay of lateral acceleration. Using simplified linear two degree-of-freedom bicycle model, simulation study and theoretical analysis are conducted to evaluate the lane-keeping control performance of active four-wheel-steering automobiles which have different desired steering responses. Finally, the evaluation is conducted on straight and curved roadway tracking maneuvers.

Lane Departure Warning System Using Top-view Space (Top-view 공간을 활용한 차선 이탈 경보 시스템)

  • Park, Han-dong;Oh, Jeong-su
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2016.05a
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    • pp.815-818
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    • 2016
  • Forward collision warning systems(FCWS) and lane departure warning systems(LDWS) need regions of interest for detecting lanes and objects as road regions. In general, the lane departure warning system using a vehicle front camera is tracking a lane curve using RANSAC or the like in the form of a straight line obtained image are compared with the center of the vehicle. This algorithm has weaknesses that requires a wide range of the lane being vulnerable to the curve. This paper presents an algorithm that checks whether the current lane departure by car from the Top-view space. The algorithm also can check whether the vehicle in the lane departure of the narrow range, and shows the result that is almost not affected by noise.

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A New Efficient Detection Method in Lane Road Environment (도로 환경에 효율적인 새로운 차선 검출 방법)

  • Lee, Kyung-Min;Lin, Chi-Ho
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.1
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    • pp.129-136
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    • 2018
  • In this paper, we propose a new real-time lane detection method that is efficient for road environment. Existing methods have a problem of low reliability under environmental changes. In order to overcome this problem, we emphasize the lane candidate area by using gray level division. And Extracts a straight line component near the lane by using the Hough transform, and generates an ROI for each straight line based on the extracted coordinates. And integrates the generated ROI images. Then, the lane is determined by dividing the object using the dual queue in the ROI image. The proposed method is able to detect lanes even in the environmental change unlike the conventional method. And It is possible to obtain an advantage that the area corresponding to the background such as sky, mountain, etc. is efficiently removed and high reliability is obtained.