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3D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卓越的3D MOT性能，基于模型的方法因其简洁性、可解释性和数据效率优势仍具吸引力。传统基于模型的跟踪器通常在检测跟踪（TBD）框架内依赖基于随机向量的贝叶斯滤波器，但受限于启发式数据关联与轨迹管理机制。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新生、存续与消亡，提升了可解释性与参数调节能力。本文提出OptiPMB——一种基于RFS的新型3D MOT方法，在TBD框架内采用优化的泊松多伯努利（PMB）滤波器，并融合多项关键创新设计。具体而言，我们提出测量驱动的混合自适应新生模型以改进轨迹初始化，采用自适应检测概率参数有效维持被遮挡目标的轨迹，并优化密度剪枝与轨迹提取模块以全面提升跟踪性能。在nuScenes和KITTI数据集上的大量实验表明，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3D MOT树立了新基准，并为自动驾驶领域基于RFS的跟踪器研究提供了重要参考。