• Title/Summary/Keyword: Road Lane Detection

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Implementation of Low-cost Autonomous Car for Lane Recognition and Keeping based on Deep Neural Network model

  • Song, Mi-Hwa
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.1
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    • pp.210-218
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    • 2021
  • CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

Lane Detection Based on a Cumulative Distribution function of Edge Direction (에지 방향의 누적분포함수에 기반한 차선인식)

  • Yi, Un-Kun;Baek, Kwang-Ryul;Lee, Joon-Woong
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2814-2818
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    • 2000
  • This paper describes an image processing algorithm capable of recognizing the road lane using a CDF (Cumulative Distribution Function). which is designed for the model function of the road lane. The CDF has distinctive peak points at the vicinity of the lane direction because of the directional and positional continuities of the lane. We construct a scatter diagram by collecting the edge pixels with the direction corresponding to the peak point of the CDF and carry out the principal axis-based line fitting for the scatter diagram to obtain the lane information. As noises play the role of making a lot of similar features to the lane appear and disappear in the image we introduce a recursive estimator of the function to reduce the noise effect and a scene understanding index (SUI) formulated by statistical parameters of the CDF to prevent a false alarm or miss detection. The proposed algorithm has been implemented in a real time on the video data obtained from a test vehicle driven in a typical highway.

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Development of A Vision-based Lane Detection System with Considering Sensor Configuration Aspect (센서 구성을 고려한 비전 기반 차선 감지 시스템 개발)

  • Park Jaehak;Hong Daegun;Huh Kunsoo;Park Jahnghyon;Cho Dongil
    • Transactions of the Korean Society of Automotive Engineers
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    • v.13 no.4
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    • pp.97-104
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    • 2005
  • Vision-based lane sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system is developed with considering sensor configuration aspects. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3-dimensional road geometry can be reconstructed in a robust manner. A new monitoring model for estimating the road geometry parameters is constructed to reduce the number of the measured signals. The selection of the sensor configuration and specifications is investigated by utilizing the characteristics of standard highways. Based on the sensor configurations, it is shown that appropriate sensing region on the camera image coordinate can be determined. The proposed system is implemented on a passenger car and verified experimentally.

A Study on the Estimation of Lane position using difference of Intensity (Intensity차를 이용한 차선의 위치 검출에 관한 연구)

  • 손경희;송현승;노도환
    • 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.

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IMAGE PROCESSING TECHNIQUES FOR LANE-RELATED INFORMATION EXTRACTION AND MULTI-VEHICLE DETECTION IN INTELLIGENT HIGHWAY VEHICLES

  • Wu, Y.J.;Lian, F.L.;Huang, C.P.;Chang, T.H.
    • International Journal of Automotive Technology
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    • v.8 no.4
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    • pp.513-520
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    • 2007
  • In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

Traversable Region Detection Algorithm using Lane Information and Texture Analysis (차로 수 정보와 텍스쳐 분석을 활용한 주행가능영역 검출 알고리즘)

  • Hwang, Sung Soo;Kim, Do Hyun
    • Journal of Korea Multimedia Society
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    • v.19 no.6
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    • pp.979-989
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    • 2016
  • Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.

A Study on the System for Lane Departure Detection Using Color Data Processing (컬러정보처리를 이용한 차선이탈경보시스템의 연구)

  • Shin Cheon Woo
    • Journal of Korea Multimedia Society
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    • v.8 no.3
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    • pp.428-437
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    • 2005
  • In this paper, we developed lane departure warning system for lane departure situation of vehicle, accidentally. Color level of yellow lane of road side is changed to black and white level. And black and white level value of relevant pixel of original and yellow lane through added process of this are emphasized. In lane departure detection system, color image data processing method could improve the recognition of the yellow lane(central lane). We could get a system Performance of the high-speed image data Processing. Therefore, lane departure warning system will be utilized at the device for the safety going of the vehicle.

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Study on Robust Driving for Autonomous Vehicle in Real-Time (자율주행차량의 실시간 강건한 주행을 위한 연구)

  • 이대은;김정훈;김영배
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2004.10a
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    • pp.908-911
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    • 2004
  • In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

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Research of the Unmanned Vehicle Control and Modeling for Lane Tracking (차선인식을 위한 무인자동차의 차량제어 및 모델링에 관한 연구)

  • 김상겸;임하영;김정하
    • Transactions of the Korean Society of Automotive Engineers
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    • v.11 no.6
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    • pp.213-221
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    • 2003
  • This paper describes a method of lane tracking by means of a vision system which includes vehicle control and modeling. Lane tracking is considered one of the important technologies in an unmanned vehicle and mobile robot system. The current position and condition of the vehicle are calculated from an image processing method by a CCD camera. We deal with lane tracking as follows. First, vehicle control is included in the road model, and lateral and longitudinal controls. Second, the image processing method deals with the lane detection method, image processing algerian, and filtering method. Finally, this paper proposes a correct method for lane detection through a vehicle test by wireless data communication.

Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera

  • Shin, Wook-Sun;Song, Doo-Heon;Lee, Chang-Hun
    • Journal of Information Processing Systems
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    • v.2 no.1
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    • pp.52-57
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    • 2006
  • One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.