基于Poisson多Bernoulli混合滤波的自主车辆多目标跟踪
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles
摘要：
自动驾驶汽车执行3D跟踪的能力对于在混乱环境中安全规划和导航至关重要。在自动驾驶应用中，多目标跟踪（MOT）的主要挑战在于关于目标的数量、目标可能出现和消失的时间和地点以及关于目标状态的不确定性的固有不确定性。基于随机有限集（RFS）的方法可以很自然地对这些不确定性进行精确和优雅的建模，并且它们已经被广泛地应用于雷达跟踪应用中。在这项工作中，我们开发了一个基于RFS的MOT框架的三维激光雷达数据。特别地，我们提出了一种泊松多伯努利混合（PMBM）滤波器来解决自动驾驶应用中的非模态MOT问题。据我们所知，这是首次尝试将基于RFS的方法与3D LiDAR数据结合用于MOT应用，并使用行业领导者提供的具有挑战性的数据集进行全面验证。我们的PMBM跟踪器在公共Waymo和Argoverse数据集上的卓越实验结果清楚地表明，基于RFS的跟踪器优于许多最先进的基于深度学习和基于卡尔曼滤波器的方法，因此，这些结果表明进一步探索基于RFS的3D MOT应用框架的巨大潜力。
英文摘要：
The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects' states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-based MOT framework for 3D LiDAR data. In partiuclar, we propose a Poisson multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for autonomous driving applications. To the best of our knowledge, this represents a first attempt for employing an RFS-based approach in conjunction with 3D LiDAR data for MOT applications with comprehensive validation using challenging datasets made available by industry leaders. The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
