  In autonomous driving, the temporal stability of 3D object detection greatly
impacts the driving safety. However, the detection stability cannot be accessed
by existing metrics such as mAP and MOTA, and consequently is less explored by
the community. To bridge this gap, this work proposes Stability Index (SI), a
new metric that can comprehensively evaluate the stability of 3D detectors in
terms of confidence, box localization, extent, and heading. By benchmarking
state-of-the-art object detectors on the Waymo Open Dataset, SI reveals
interesting properties of object stability that have not been previously
discovered by other metrics. To help models improve their stability, we further
introduce a general and effective training strategy, called Prediction
Consistency Learning (PCL). PCL essentially encourages the prediction
consistency of the same objects under different timestamps and augmentations,
leading to enhanced detection stability. Furthermore, we examine the
effectiveness of PCL with the widely-used CenterPoint, and achieve a remarkable
SI of 86.00 for vehicle class, surpassing the baseline by 5.48. We hope our
work could serve as a reliable baseline and draw the community's attention to
this crucial issue in 3D object detection. Codes will be made publicly
available.
