  Reliable uncertainty estimation is crucial for perception systems in safe
autonomous driving. Recently, many methods have been proposed to model
uncertainties in deep learning based object detectors. However, the estimated
probabilities are often uncalibrated, which may lead to severe problems in
safety critical scenarios. In this work, we identify such uncertainty
miscalibration problems in a probabilistic LiDAR 3D object detection network,
and propose three practical methods to significantly reduce errors in
uncertainty calibration. Extensive experiments on several datasets show that
our methods produce well-calibrated uncertainties, and generalize well between
different datasets.

Robotics;Computer Vision and Pattern Recognition