IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Construction of LiDAR Dataset for Autonomous Driving Considering Domestic Environments and Design of Effective 3D Object Detection Model

국내 주행환경을 고려한 자율주행 라이다 데이터 셋 구축 및 효과적인 3D 객체 검출 모델 설계

  • Received : 2023.06.20
  • Accepted : 2023.08.09
  • Published : 2023.10.31
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Abstract

Recently, with the growing interest in the field of autonomous driving, many researchers have been focusing on developing autonomous driving software platforms. In particular, we have concentrated on developing 3D object detection models that can improve real-time performance. In this paper, we introduce a self-constructed 3D LiDAR dataset specific to domestic environments and propose a VariFocal-based CenterPoint for the 3D object detection model, with improved performance over the previous models. Furthermore, we present experimental results comparing the performance of the 3D object detection modules using our self-built and public dataset. As the results show, our model, which was trained on a large amount of self-constructed dataset, successfully solves the issue of failing to detect large vehicles and small objects such as motorcycles and pedestrians, which the previous models had difficulty detecting. Consequently, the proposed model shows a performance improvement of about 1.0 mAP over the previous model.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부에서 지원하는 대구경북과학기술원 기관고유사업 (23-IT-02)과 기술사업화 역량강화사업 (2023-DG-RD-0041) 지원을 받아 수행 되었습니다.

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