  The capability to detect objects is a core part of autonomous driving. Due to
sensor noise and incomplete data, perfectly detecting and localizing every
object is infeasible. Therefore, it is important for a detector to provide the
amount of uncertainty in each prediction. Providing the autonomous system with
reliable uncertainties enables the vehicle to react differently based on the
level of uncertainty. Previous work has estimated the uncertainty in a
detection by predicting a probability distribution over object bounding boxes.
In this work, we propose a method to improve the ability to learn the
probability distribution by considering the potential noise in the ground-truth
labeled data. Our proposed approach improves not only the accuracy of the
learned distribution but also the object detection performance.
