  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.
