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자율주행 시스템의 Pure-pursuit 알고리즘을 이용한 Path Management 시스템 개발
최윤중(YunJung Choi),김현우(Hyunwoo Kim),신희석(HeeSeok Shin),김명준(MyeongJun Kim),김상준(SangJun Kim),길현준(HyunJun Gil),서주원(JuWon Seo),박선영(SunYoung Park),김재일(Jaell Kim),임효진(HyoJin Lim),김정하(JungHa Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
Research on autonomous vehicles consists of perception, judgement, and control. This paper is about the Ld method of the Pure-pursuit algorithm among the control algorithms based on the kinematic method in the control field. In the process of finding the correct L<SUB>d</SUB> for actual control, an error occurred between the detected L<SUB>d</SUB> value and the correct L<SUB>d</SUB>, and only a part of the path was detected L<SUB>d</SUB>. In order to store only the coordinates to be detected by LD in the path reference, the interval between paths was set very small, and the average interval and error of the detected LD values were calculated. The average interval and error of the detected LD value was proportional to the distance the sensor moved during the period of receiving a new signal, and accordingly, the path interval and path error were set appropriately. In the existing algorithm, the optimal interval and error are determined using the experimental data of the vehicle. The algorithm in this study may be less accurate than the existing algorithm. However, using only the target speed, the period of the sensor, and the minimum turning radius of the vehicle, it is possible to set the distance and the error of the path with high accuracy. Finally, the verification was carried out by directly controlling the autonomous vehicle.
Graph SLAM을 통한 산악형 도심지역의 고정밀지도 작성 및 위치 인식 시스템에 대한 연구
김현우(Hyunwoo Kim),최윤중(YunJung Choi),강동완(DongWan Kang),김상준(SangJun Kim),길현준(HyunJun Gil),서주원(JuWon Seo),박선영(SunYoung Park),김재일(JaeIl Kim),임효진(HyoJin Lim),김정하(JungHa Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper suggests a localization system architecture of unmanned vehicle for autonomous driving. In this system, Graph SLAM algorithm is used for correction of accumulated errors acquired from scan matching algorithm. On this paper, the experiments are proceeded in environment of mountainous city which have continuous elevations. The acceleration and magnetic data from IMU, RTK corrected GNSS are used for graph optimization and loop closure on mapping. NDT scan matching algorithm is used for localization.
라이다-카메라 캘리브레이션을 통한 동적 장애물 회피를 위한 자율주행 인지시스템에 대한 연구
김현우(Hyunwoo Kim),최윤중(YunJung Choi),김상준(SangJun Kim),길현준(HyunJun Gil),서주원(JuWon Seo),박선영(SunYoung Park),김재일(Jaell Kim),임효진(HyoJin Lim),김정하(JungHa Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
Sensor fusion area are known to great solution for the edge cases of perception system of automated car. This system uses camera and LiDAR on front side of the vehicle and integrated on a coordinate system for the projection. Each point data on image pixel integrated point cloud gets its class by semantic segmentation. This paper suggests a perception system of autonomous vehicle for moving objects by deep learning based image semantic segmentation and lidar points projection to camera pixel image.
LiDAR를 활용한 ROS 기반 ACC 인지 시스템 및 NDT-Localization 시스템에 대한 연구
김상준(Sangjun Kim),길현준(Hyeonjun Gil),최윤중(Yunjung Choi),김정하(Jungha Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper proposes ROS-Based ACC Recognition System and NDT-Localization System using LiDAR Sensor. The platform selected as the sub-controller is ERP-42, which informs the current speed, steering angle, and driving mode of the vehicle. An industrial PC is combined on the vehicle and used as a upper-Level controller. In the upper- Level controller, object detection and localization through LiDAR Sensor are published in the topic form on ROS. The vehicle performs object detection and localization through real-time communication using the corresponding topic. In conclusion, this system enables object detection and localization for ACC with one PC. By developing this study, it will be possible to build a optimized perception system using LiDAR Sensor.
V2I 기반 3D LiDAR pointcloud 정밀지도 통신을 통한 자율주행자동차의 실시간 Localization 시스템
김명준(Myeong-jun Kim),이동훈(Donghun Lee),최윤중(Yunjung Choi),김정하(Jungha Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
In order for autonomous vehicles to localize using LiDAR sensors, a 3D map is needed. A high definition 3D map occupies a very large capacity. Therefore, the computing power of the autonomous vehicle may be overloaded. HD map is also needed to perform accurate localization in a new place. To solve this problem, this paper proposes a V2I-based real-time HD map telecommunication and localization system. Using this system, autonomous vehicles can receive new maps from the surrounding infrastructure while driving and then localize them.
A Research on Fail-Safe System by Watch Dog for Multi Sensor Fused Autonomous Vehicle
Sunyoung Park(박선영),Hyunjun Gil(길현준),Sangjun Kim(김상준),Juwon Seo(서주원),Yunjung Choi(최윤중),Hyunwoo Kim(김현우),Jungha Kim(김정하) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
In this study, the Watch Dog monitoring system was designed by installing ROS Melodic based on Ubuntu 18.04. Watch Dog is a system that detects programatic errors in autonomous driving controllers and manages sensors such as LiDAR, Camera, GPS, and Imu in real time. First, Watch Dog checks the sensor for power and lan connections by default. It also analyzes the reliability of the data obtained from NDT and GPS and the speed difference between GPS and encoder to determine whether localization and mapping are going well. Visual monitoring is performed to recognize the part of the problem in the event of an error, and the control command is passed on to the control PC after determining whether it is possible to drive.
NDT를 이용한 실내 Tractor-Trailer 자동 주차 알고리즘 개발
신희석(Heeseok Shin),장성빈(Sung Been Jang),장재익(Jeaik Jang),김현우(Hyunwoo Kim),최윤중(YunJung Choi),김정하(Jung-Ha Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper is about automatically parking indoors through localization using LiDAR in an indoor where GPS is not received. The indoor localization method used the NDT algorithm, and the hitch angle of the trailer was detected using the camera. The position of the tractor was detected using the NDT algorithm, and the position and angle of the trailer were calculated by applying the trailer model based on the hitch angle detected by the camera. The trailer was parked in the designated parking space by following the finally created path.