  Reliable uncertainty estimation is crucial for robust object detection in
autonomous driving. However, previous works on probabilistic object detection
either learn predictive probability for bounding box regression in an
un-supervised manner, or use simple heuristics to do uncertainty
regularization. This leads to unstable training or suboptimal detection
performance. In this work, we leverage our previously proposed method for
estimating uncertainty inherent in ground truth bounding box parameters (which
we call label uncertainty) to improve the detection accuracy of a probabilistic
LiDAR-based object detector. Experimental results on the KITTI dataset show
that our method surpasses both the baseline model and the models based on
simple heuristics by up to 3.6% in terms of Average Precision.
