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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.
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.