  3D multi-object tracking is a key module in autonomous driving applications
that provides a reliable dynamic representation of the world to the planning
module. In this paper, we present our on-line tracking method, which made the
first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics
Workshop at NeurIPS 2019. Our method estimates the object states by adopting a
Kalman Filter. We initialize the state covariance as well as the process and
observation noise covariance with statistics from the training set. We also use
the stochastic information from the Kalman Filter in the data association step
by measuring the Mahalanobis distance between the predicted object states and
current object detections. Our experimental results on the NuScenes validation
and test set show that our method outperforms the AB3DMOT baseline method by a
large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric.
