【摘要】 多目标跟踪是保障自动驾驶行驶安全与效率的基础，其获取的数据广泛应用于自动驾驶运动规划、驾驶决策等上层应用。传统多目标跟踪方法在遮挡环境下往往存在目标丢失、错位的现象，针对这一问题，本文提出了基于异源雷达融合与遮挡预测模型的鲁棒跟踪方法。首先，基于激光雷达与毫米波雷达的局部观测一致性方程，提出了一种基于多目标运动约束与全局最大匹配的多传感动态自标定算法。其次，针对完全遮挡环境下因观测数据缺失导致的跟踪中断问题，提出了一种基于异构雷达融合无迹卡尔曼滤波与长短期时序神经网络的混合监督目标位置预测方法。通过实验表明，本文提出的方法在完全遮挡环境下可有效补全至少81%断开的多车辆目标轨迹，且相比于最先进的方法，能够实现更为可靠的多交通目标跟踪。更多还原

【Abstract】 Multi-target tracking is the basis to ensure the safety and efficiency of autonomous driving, and the obtained data are widely used in the upper applications of autonomous driving, such as motion planning and driving decision making. The traditional multi-target tracking method often has the phenomenon of target loss and dislocation in the occlusion environment. To solve the problem, a robust tracking method based on heterogeneous radar fusion and occlusion prediction model is proposed. Firstly, based on the local observation consistency equation of lidar and millimeter-wave radar, a multi-sensor dynamic self-calibration algorithm based on multi-target motion constraint and global maximum matching is proposed. Secondly, a hybrid supervised target position prediction method based on heterogeneous radar fusion unscented Kalman filter and long and short time series neural network is proposed to solve the problem of tracking interruption caused by missing observation data in complete occlusion environment. Experiments show that the proposed method can effectively complete at least 81% of the broken multi-vehicle target tracks in the complete occlusion environment, which can achieve more reliable multi-vehicle target tracking compared with the most advanced methods.更多还原