Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving.

翻译：精确的三维多目标跟踪（MOT）对于自动驾驶至关重要，它能在复杂环境中实现鲁棒的感知、导航与规划。尽管基于深度学习的解决方案已展现出令人印象深刻的三维MOT性能，但基于模型的方法因其简洁性、可解释性和数据效率而依然具有吸引力。传统的基于模型跟踪器通常在检测跟踪（TBD）框架内依赖基于随机矢量的贝叶斯滤波器，但由于启发式数据关联与航迹管理方案而面临局限。相比之下，基于随机有限集（RFS）的贝叶斯滤波以理论完备的方式处理目标的产生、存续与消亡，从而提升了可解释性与参数调优的便利性。本文提出OptiPMB，一种新颖的基于RFS的三维MOT方法，它采用优化的泊松多伯努利（PMB）滤波器，并在TBD框架内融入了多项关键创新设计。具体而言，我们提出了一种测量驱动的混合自适应新生模型以改进航迹初始化，采用自适应检测概率参数以有效维持被遮挡目标的航迹，并优化了密度剪枝与航迹提取模块以进一步提升整体跟踪性能。在nuScenes和KITTI数据集上的大量评估表明，OptiPMB相比现有先进方法实现了更优的跟踪精度，从而为基于模型的三维MOT设立了新基准，并为未来自动驾驶中基于RFS的跟踪器研究提供了有价值的见解。