• Title/Summary/Keyword: 차선 변경

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차량의 자동주행을 위한 목표물 추적 알고리듬: AIMM-UKF

  • 김용식;홍금식
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2004.05a
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    • pp.166-166
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    • 2004
  • 운전자 보조시스템에는 적응순항제어 (adaptive cruise control), 차선변경 (lane change), 충돌경고 (collision warning), 충돌회피 (collision avoidance), 및 자동주차 (automatic parking) 등이 있다. 이런 운전자 보조시스템은 어떤 목적을 가지고 있다. 운전자의 부담을 줄이고 안전을 위하여 차량의 주행방향에 있는 장애물이나 차량을 감지하여 차량간의 안전거리론 유지하고 자동차가 일정 속도를 유지하도록 한다. 운전자 보조시스템의 효율은 센서들로부터 얻어진 정보의 해석에 달려있다.(중략)

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Verification of an automatic turn signal system for a bicyclist safety using Proteus VSM simulation (프로테우스 VSM을 이용한 자전거 운전자 안전을 위한 자동 방향 표시 시스템 검증)

  • Yoo, Jae-Duck;Kang, Hee-Hoon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.10 no.5
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    • pp.637-644
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    • 2015
  • Bicycles share the road where high speed cars run and driving license is essential to bicyclist. After sunset, accidents can be occurred due to changing one line to another line in a road inadequately and bicycle driving route changing without an alarm. In this paper, we apply the same system as an automatic signal lights system to prevent the accidents for bicyclist safety. Then, we verify it using proteus VSM simulation.

Analysis System for Public Interest Report Video of Traffic Law Violation based on Deep Learning Algorithms (딥러닝 알고리즘 기반 교통법규 위반 공익신고 영상 분석 시스템)

  • Min-Seong Choi;Mi-Kyeong Moon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.1
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    • pp.63-70
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    • 2023
  • Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.

A Study on The Detection of Multiple Vehicles Using Sequence Image Analysis (연속 영상 분석에 의한 다중 차량 검출 방법의 연구)

  • 한상훈;이강호
    • Journal of the Korea Society of Computer and Information
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    • v.8 no.2
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    • pp.37-43
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    • 2003
  • The purpose of this thesis is to detect multiple vehicles using sequence image analysis at process that detect forward vehicles and lane from sequential color images. Detection of vehicles candidate area uses shadow characteristic and edge information in one frame. And, method to detect multiple vehicles area analyzes Estimation of Vehicle(EOV) and Accumulated Similarity Function(ASF) of vehicles candidate areas that exist in sequential images and examine possibility to be vehicles. Most researches detected a forward vehicles in road images but this research presented method to detect several vehicles and apply enough in havy traffic. To verify the effects of the proposed method, we capture the road images with notebook and CCD camera for PC and present the results such as processing time, accuracy and vehicles detection in the images.

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Driving three kinds of Course Test with RC car by Color Recognition (색깔 인식에 의한 RC car의 3가지 코스 시험 주행)

  • Lee, Jong-Min;Sohn, Young-Sun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.1
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    • pp.33-39
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    • 2014
  • Automatic driving needs many functions such as the obstacle recognition, the lane recognition, and the lane change, etc. In this paper, we realized a system which automatically drove the three-kinds of vehicle driving course, to introduce and apply the concept of 'color recognition' that expands the scope of 'lane recognition' for vehicle driving. We made the reduced each course compared with RC(Radio Control) car size, and controlled the steering considering the position and the slope of the detection line and the speed. Because the RC car does not have the brake function, we consider the speed and the position of the detection line to stop the RC car.

A Research of Obstacle Detection and Path Planning for Lane Change of Autonomous Vehicle in Urban Environment (자율주행 자동차의 실 도로 차선 변경을 위한 장애물 검출 및 경로 계획에 관한 연구)

  • Oh, Jae-Saek;Lim, Kyung-Il;Kim, Jung-Ha
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.2
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    • pp.115-120
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    • 2015
  • Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

Advanced Driver Assistance System for the Control of Turn Signal Indicator (방향지시등 제어를 위한 운전자 지원 시스템)

  • Kim, Dae-Soon
    • Journal of IKEEE
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    • v.22 no.1
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    • pp.143-148
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    • 2018
  • In this paper, a novel turn signal indication scheme is proposed and implemented to handle the turn signal of a vehicle automatically. By adopting accelerometer for the motion recognition of vehicle's momentum, the proposed way could control and manage turn signals according to the moving direction of a car when a driver forgot handling turn signal lever. The designed control system is plugged into the motorbike and tested to demonstrate improved driver's safety suitable for ADAS.

A Study On Driver Model far Steering Simulation of Vehicle (차량의 조향 시뮬레이션을 위한 운전자 모델에 대한 연구)

  • ;;;Ichiro Kageyama
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.3
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    • pp.245-253
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    • 2002
  • A driver model with nervous neuromuscular system was developed to steer a vehicle along the prescribed path during handling simulations. A 3-dimensional vehicle model with 10 DOF and 3 DOF steering handle are used to perform a computer simulation. PID and fuzzy controller are used to perform single and double lane change, and their tracking abilities were compared. The effects of time delay and preview distance are also investigated, and it is demonstrated that the driver model developed can be an aid far objective evaluation of vehicle handling simulation.

Dynamic Simulation using the Driver Model to Evaluate the Handling Performances (운전자 모델을 이용한 조종안정성능 평가 시뮬레이션)

  • 손희성
    • Transactions of the Korean Society of Automotive Engineers
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    • v.5 no.5
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    • pp.1-8
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    • 1997
  • The purpose of this study is to suggest the methods to model driver input and evaluate the handling performances of a vehicle by dynamic simulation using ADAMS (Automated Dynamic Analysis of Mechanical Systems) software. The driver input was modeled using the PID controller to follow the desired velocities and paths. The gains of the controller were decided by the trial and error methods aided by Ziegler-Nichols rule. It was successful to apply the rule for the vehicle model to follow the desired values of steady state cornering and lane change maneuver. As the results, handling performances of baseline and two variegated vehicles were evaluated. The theoretical provement was performed to explain the differences.

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A Lane Change Recognition System for Smart Cars (스마트카를 위한 차선변경 인식시스템)

  • Lee, Yong-Jin;Yang, Jeong-Ha;Kwak, Nojun
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.1
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    • pp.46-51
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    • 2015
  • In this paper, we propose a vision-based method to recognize lane changes of an autonomous vehicle. The proposed method is based on six states of driving situations defined by the positional relationship between a vehicle and its nearest lane detected. With the combinations of these states, the lane change is detected. The proposed method yields 98% recognition accuracy of lane change even in poor situations with partially invisible lanes.