도심 환경 속 판단 보조를 위한 교통섬 지형분할 및 객체분류

다국어 초록 (Multilingual Abstract)

In the urban environment, terrains such as a “traffic island” are present for the convenience and safety of pedestrians. However, such terrains cause decision errors in the autonomous system. In the existing autonomous vehicles, a method of stopping or decelerating the vehicle for safety when an object is detected on the driving path or around the vehicle is applied. However, such methods not only make it difficult for the autonomous vehicles to drive smoothly in unusual situations such as a traffic island but also cause traffic jam with the possibility to cause traffic accidents in the urban environment. To mitigate this problem, we segmented the traffic island and roadway through semantic segmentation. Pedestrians on the traffic island are classified as the “safety group,” and the other areas are termed as the “non-safety group,” providing an efficient method for stable autonomous driving decisions in the Pangyo area.

목차 (Table of Contents)

• Abstract
• Ⅰ. 서론
• Ⅱ. 관련 연구
• Ⅲ. 실험 차량 소개
• Ⅳ. 안전군과 비안전군 검출 알고리즘
• Ⅴ. 구현 및 결과
• Ⅵ. 결론
• Ⅶ. 향후 과제
• REFERENCES

참고문헌 (Reference)

1 이종서, "포인트 클라우드 보간을 이용한 카메라-라이다의 센서 퓨전 기반 객체 검출" 제어·로봇·시스템학회 26 (26): 469-478, 2020

2 M. Heinz, "The three-dimensional normal-distributions transform" 10 : 3-, 2008

3 B. Schoettle, "Sensor fusion: A comparison of sensing capabilities of human drivers and highly automated vehicles" University of Michigan 2017

4 T. D. Stoyanov, "Reliable autonomous navigation in semistructured environments using the three-dimensional normal distributions transform (3d-ndt)" Örebro University 2012

5 W. Wen, "Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong" 18 (18): 3928-, 2018

6 P. Soviany, "Optimizing the trade-off between single-stage and two-stage deep object detectors using image difficulty prediction" IEEE 2018

7 K. He, "Mask r-cnn" 2017

8 A. Dhall, "LiDAR-camera calibration using 3D-3D point correspondences"

9 F. Camara, "Human Factors in Intelligent Vehicles" 2020

10 A. Paigwar, "Gndnet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles" IEEE 2020

11 T. -Y. Lin, "Feature pyramid networks for object detection" 2017

12 Y. Wu, "Detectron2"

13 J. Hariyono, "Detection of pedestrian crossing road using action classification model" IEEE 2015

14 Sukru Yaren Gelbal, "COLLISION AVOIDANCE OF LOW SPEED AUTONOMOUS SHUTTLES WITH PEDESTRIANS" 한국자동차공학회 21 (21): 903-917, 2020

15 D. F. Llorca, "Autonomous pedestrian collision avoidance using a fuzzy steering controller" 12 (12): 390-401, 2011

16 S. Kuutti, "A survey of the state-ofthe-art localization techniques and their potentials for autonomous vehicle applications" 5 (5): 829-846, 2018

17 S. Grigorescu, "A survey of deep learning techniques for autonomous driving" 37 (37): 362-386, 2020

18 염상식, "2D 시맨틱 분할 프로젝션을 이용한 3D 실내 공간 시맨틱 분할" 제어·로봇·시스템학회 26 (26): 949-954, 2020