  Sharing information between connected and autonomous vehicles (CAVs)
fundamentally improves the performance of collaborative object detection for
self-driving. However, CAVs still have uncertainties on object detection due to
practical challenges, which will affect the later modules in self-driving such
as planning and control. Hence, uncertainty quantification is crucial for
safety-critical systems such as CAVs. Our work is the first to estimate the
uncertainty of collaborative object detection. We propose a novel uncertainty
quantification method, called Double-M Quantification, which tailors a moving
block bootstrap (MBB) algorithm with direct modeling of the multivariant
Gaussian distribution of each corner of the bounding box. Our method captures
both the epistemic uncertainty and aleatoric uncertainty with one inference
pass based on the offline Double-M training process. And it can be used with
different collaborative object detectors. Through experiments on the
comprehensive collaborative perception dataset, we show that our Double-M
method achieves more than 4X improvement on uncertainty score and more than 3%
accuracy improvement, compared with the state-of-the-art uncertainty
quantification methods. Our code is public on
https://coperception.github.io/double-m-quantification.
