  Object detection and multiple object tracking (MOT) are essential components
of self-driving systems. Accurate detection and uncertainty quantification are
both critical for onboard modules, such as perception, prediction, and
planning, to improve the safety and robustness of autonomous vehicles.
Collaborative object detection (COD) has been proposed to improve detection
accuracy and reduce uncertainty by leveraging the viewpoints of multiple
agents. However, little attention has been paid to how to leverage the
uncertainty quantification from COD to enhance MOT performance. In this paper,
as the first attempt to address this challenge, we design an uncertainty
propagation framework called MOT-CUP. Our framework first quantifies the
uncertainty of COD through direct modeling and conformal prediction, and
propagates this uncertainty information into the motion prediction and
association steps. MOT-CUP is designed to work with different collaborative
object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a
comprehensive collaborative perception dataset, and demonstrate a 2%
improvement in accuracy and a 2.67X reduction in uncertainty compared to the
baselines, e.g. SORT and ByteTrack. In scenarios characterized by high
occlusion levels, our MOT-CUP demonstrates a noteworthy $4.01\%$ improvement in
accuracy. MOT-CUP demonstrates the importance of uncertainty quantification in
both COD and MOT, and provides the first attempt to improve the accuracy and
reduce the uncertainty in MOT based on COD through uncertainty propagation. Our
code is public on https://coperception.github.io/MOT-CUP/.
