| Autor: |
WANG Zeyuan, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, XIONG Qunfang |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Kongzhi Yu Xinxi Jishu, Iss 5, Pp 25-32 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2096-5427 |
| DOI: |
10.13889/j.issn.2096-5427.2023.05.005 |
| Popis: |
The 3D perception system functions as one of the core components for the safe operation of autonomous-rail rapid tram. Addressing the limitations of LiDAR systems used for autonomous-rail rapid tram in complex urban environments, such as challenges in perceiving distant objects, insensitivity to color, and potential failure, this paper proposes a pure-vision monocular 3D perception system for autonomous-rail rapid tram. Comprising data preprocessing, model training and model deployment, this vision-based scheme with a full coverage from data collection to on-board deployment enables the 3D perception of obstacles around autonomous-rail rapid tram, thereby improving the operational reliability in complex urban environments. The test results based on the autonomous-rail rapid transit dataset and Waymo open dataset show that the system can effectively perceive the complex road scenes in autonomous-rail rapid transit, achieving a final 3D average precision (AP) of 0.53, reasoning a single-frame of image about 56 ms, and meeting the real-time requirements of autonomous-rail rapid tram for the obstacle perception algorithm. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
