  Multi-object tracking is an important ability for an autonomous vehicle to
safely navigate a traffic scene. Current state-of-the-art follows the
tracking-by-detection paradigm where existing tracks are associated with
detected objects through some distance metric. The key challenges to increase
tracking accuracy lie in data association and track life cycle management. We
propose a probabilistic, multi-modal, multi-object tracking system consisting
of different trainable modules to provide robust and data-driven tracking
results. First, we learn how to fuse features from 2D images and 3D LiDAR point
clouds to capture the appearance and geometric information of an object.
Second, we propose to learn a metric that combines the Mahalanobis and feature
distances when comparing a track and a new detection in data association. And
third, we propose to learn when to initialize a track from an unmatched object
detection. Through extensive quantitative and qualitative results, we show that
when using the same object detectors our method outperforms state-of-the-art
approaches on the NuScenes and KITTI datasets.
