发表机构
National Key Laboratory of Radar Signal Processing, Xidian University; Department of Electrical and Computer Engineering, National University of Singapore（西安电子科技大学雷达信号处理全国重点实验室; 新加坡国立大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种解耦朝向与轴尺度估计的轨迹CPHD滤波器，分别建模运动学、朝向和尺度状态，以提升多车辆转弯时的朝向、质心和足迹估计精度，并通过仿真和实测数据验证了有效性。
AI 中文摘要
可靠地估计车辆朝向、质心和足迹，对于在交叉口和其他常见道路操作中车辆转弯时表示道路使用者占用情况非常重要。在转弯过程中，车辆朝向和运动方向可能不同，将朝向与轴尺度耦合会降低朝向和范围估计的质量。本文提出了一种具有解耦朝向与轴尺度估计（DOAM）模型的轨迹基数化概率假设密度滤波器，用于多车辆跟踪。该模型在每个轨迹分量内分别表示运动学、朝向和半轴长度平方状态，减少了朝向变化与尺度估计之间的相互干扰，而无需引入额外的交互式多模型结构。一种结构化的坐标上升变分推断递归联合更新这些状态序列和测量源变量。推导了与解耦表示相关的轨迹分量似然和权重更新，同时保留了标准基数递归。固定滞后轨迹实现进一步利用当前测量来修正平滑窗口内的历史状态。使用信号交叉口仿真和真实车载激光雷达测量的评估显示，车辆朝向、质心和范围的估计得到改善，尤其是在转弯期间。所得的车辆状态和足迹估计为道路使用者占用感知以及后续的碰撞风险评估和运动规划提供信息。
英文摘要
Reliable estimation of vehicle orientation, centroid, and footprint is important for representing road-user occupancy during vehicle turns at intersections and other common road maneuvers. During a turn, the vehicle orientation and direction of motion may differ, and coupling the orientation with the axial scales can degrade both orientation and extent estimates. This paper proposes a trajectory cardinalized probability hypothesis density filter with a Decoupled Orientation and Axial-Scale Estimation (DOAM) model for multiple-vehicle tracking. The model represents the kinematic, orientation, and squared semi-axis-length states separately within each trajectory component, reducing the mutual interference between orientation variation and scale estimation without introducing an additional interacting multiple-model structure. A structured coordinate-ascent variational inference recursion jointly updates these state sequences and the measurement-source variables. The trajectory-component likelihood and weight update associated with the decoupled representation are derived while retaining the standard cardinality recursion. A fixed-lag trajectory implementation further uses current measurements to correct historical states within the smoothing window. Evaluation with signalized-intersection simulations and real onboard LiDAR measurements shows improved estimation of vehicle orientation, centroid, and extent, particularly during turns. The resulting vehicle-state and footprint estimates provide information for road-user occupancy perception and subsequent collision-risk assessment and motion planning.
Comments32 pages in total, including a 15-page main article and 17 pages of supplementary material; 7 figures and 5 tables in the main article
