A late fusion framework for multivehicle collaborative perception
Zengwen Li, Yingying Gao, Hongzhen Lv, Changxue Chen, Peng Tao, Jingyu Zeng · 2024
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.