A late fusion framework for multivehicle collaborative perception

Zengwen Li; Yingying Gao; Huaxin Lv; Changxue Chen; Peng Tao; Jingyu Zeng

doi:10.1117/12.3050430

24 October 2024 A late fusion framework for multivehicle collaborative perception

Zengwen Li, Yingying Gao, Huaxin Lv, Changxue Chen, Peng Tao, Jingyu Zeng

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960D (2024) https://doi.org/10.1117/12.3050430
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

In connected multi-vehicle system, collaborative perception and fusion technologies serve to compensate for the limitations inherent in single vehicle sensors. It can effectively combine the filtering and tracking results from sensors by fusing. However, existing multi-vehicle fusion methods do not adequately address the challenges due to the inherent inaccuracies in single vehicle data fusion and the differences in perception data among vehicles in real-world multi-vehicle scenarios. To address these challenges, we propose a fusion framework for real multiple connected vehicle scenarios. First, perceived target data are associated by the Unscented Kalman Filter (UKF) and trajectories are generated by the Hungarian algorithm. Second, generated trajectories are deduplicated and fused using methods based on Hausdorff distance with average distance complementation. Experiments conducted on real-world scenarios show a notable enhancement in fused target position accuracy and motion state accuracy. This improvement can significantly contribute to bolstering the overall driving experience through enhanced driver assistance, human-machine interaction, and other end-of-vehicle applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zengwen Li, Yingying Gao, Huaxin Lv, Changxue Chen, Peng Tao, and Jingyu Zeng "A late fusion framework for multivehicle collaborative perception", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960D (24 October 2024); https://doi.org/10.1117/12.3050430

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KEYWORDS

Sensors

Detection and tracking algorithms

Data fusion

Roads

Signal filtering

3D tracking

Interpolation

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