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Fusion Positioning System Based on IMU and Roadside LiDAR in Tunnel for C-V2X Use
Technical Paper
2020-01-5184
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
High-precision positioning is an indispensable function of connecting vehicles (CVs) and autonomous vehicles (AVs). In general, the connected vehicles based on C-V2X communication technology rely heavily on absolute positioning data. Compared with ordinary intelligent vehicles, high-precision positioning data is more important for networked vehicles. The high-precision positioning function could be obtained by integrating Global Navigation Satellite System (GNSS), Ground-based Augmentation System (GBAS), inertial measurement unit (IMU), wheel speed sensor and other position or attitude sensors in the open area. However, it is still difficult to obtain accurate absolute position information in a signal shielding area such as a tunnel, especially for high speed vehicle use. To solve the problem, this paper designs and implements a fusion positioning system (FPS) based on IMU and roadside LiDAR data, and is mainly used for C-V2X tunnel scenarios. An IMU and roadside LiDAR fusion positioning method is also proposed for enhancing the accuracy of positioning. The field experiments compared with RTK and single point positioning show that, in the tunnel scenarios, the average position error of the proposed method is sub-meter level, and is the lowest and most stable, which can meet the needs of the C-V2X security application for CVs.
Authors
- Bosi Wang - Chongqing University, China
- Dihua Sun - Chongqing University, China
- Hui Zu - Chongqing Vehicle Test & Research Institute Co., Ltd., C
- Chao Wu - Chongqing Vehicle Test & Research Institute Co., Ltd., C
- Disi Zhang - Chongqing Vehicle Test & Research Institute Co., Ltd., C
- Xinhai Chen - Chongqing Vehicle Test and Research Institute, China
Topic
Citation
Wang, B., Sun, D., Zu, H., Wu, C. et al., "Fusion Positioning System Based on IMU and Roadside LiDAR in Tunnel for C-V2X Use," SAE Technical Paper 2020-01-5184, 2020, https://doi.org/10.4271/2020-01-5184.Data Sets - Support Documents
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