  3D multiple object tracking (MOT) plays a crucial role in autonomous driving
perception. Recent end-to-end query-based trackers simultaneously detect and
track objects, which have shown promising potential for the 3D MOT task.
However, existing methods overlook the uncertainty issue, which refers to the
lack of precise confidence about the state and location of tracked objects.
Uncertainty arises owing to various factors during motion observation by
cameras, especially occlusions and the small size of target objects, resulting
in an inaccurate estimation of the object's position, label, and identity. To
this end, we propose an Uncertainty-Aware 3D MOT framework, UA-Track, which
tackles the uncertainty problem from multiple aspects. Specifically, we first
introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty
in object prediction with probabilistic attention. Secondly, we propose an
Uncertainty-guided Query Denoising strategy to further enhance the training
process. We also utilize Uncertainty-reduced Query Initialization, which
leverages predicted 2D object location and depth information to reduce query
uncertainty. As a result, our UA-Track achieves state-of-the-art performance on
the nuScenes benchmark, i.e., 66.3% AMOTA on the test split, surpassing the
previous best end-to-end solution by a significant margin of 8.9% AMOTA.
