Probabilistic 3D Multi-Object Tracking for Autonomous Driving
This work addresses reliable dynamic world representation for autonomous driving planning, but it is incremental as it builds on existing Kalman Filter methods.
The paper tackles 3D multi-object tracking for autonomous driving by using a Kalman Filter with covariance initialization and Mahalanobis distance for data association, achieving first place in the NuScenes Tracking Challenge and outperforming the AB3DMOT baseline in AMOTA.
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.