Towards Robust Reference System for Autonomous Driving: Rethinking 3D MOT

With the rapid development of autonomous driving, the need for auto-labeling reference systems is becoming increasingly urgent. 3D multiple object tracking (MOT) is one of the most critical components of the reference system. In this work, we reviewed and rethought the common failure sources and limitations of the SOTA 3D MOT methods. We propose a set of innovative 3D MOT post-processing modules as a unified framework based on the observation. First, we design a self-learning-based detector to eliminate the outliers in each tracklet. Then a novel post-processing module, GGTrajRec, will recover the breakpoints and ID switches in the trajectories. Finally, a confidence-guided trajectory optimizer is implemented to ensure each trajectory's consistency. Extensive experiments on KITTI and nuScenes show that our method can improve the SOTA methods on most evaluation metrics by a remarkable margin. Currently, our results are second ranking on the KITTI tracking leaderboard. Specifically, our method offers the lowest FPs, highest DetRe, and AssRe values among all methods, which can significantly contribute to a stable and robust reference system for ADAS.