  We present a robust real-time LiDAR 3D object detector that leverages
heteroscedastic aleatoric uncertainties to significantly improve its detection
performance. A multi-loss function is designed to incorporate uncertainty
estimations predicted by auxiliary output layers. Using our proposed method,
the network ignores to train from noisy samples, and focuses more on
informative ones. We validate our method on the KITTI object detection
benchmark. Our method surpasses the baseline method which does not explicitly
estimate uncertainties by up to nearly 9% in terms of Average Precision (AP).
It also produces state-of-the-art results compared to other methods while
running with an inference time of only 72 ms. In addition, we conduct extensive
experiments to understand how aleatoric uncertainties behave. Extracting
aleatoric uncertainties brings almost no additional computation cost during the
deployment, making our method highly desirable for autonomous driving
applications.
