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In this paper, we propose a novel form of the loss function to increase theperformance of LiDAR-based 3d object detection and obtain more explainable andconvincing uncertainty for the prediction. The loss function was designed usingcorner transformation and uncertainty modeling. With the new loss function, theperformance of our method on the val split of KITTI dataset shows up to a 15%increase in terms of Average Precision (AP) comparing with the baseline usingsimple L1 Loss. In the study of the characteristics of predicted uncertainties,we find that generally more accurate prediction of the bounding box is usuallyaccompanied by lower uncertainty. The distribution of corner uncertaintiesagrees on the distribution of the point cloud in the bounding box, which meansthe corner with denser observed points has lower uncertainty. Moreover, ourmethod also learns the constraint from the cuboid geometry of the bounding boxin uncertainty prediction. Finally, we propose an efficient Bayesian updatingmethod to recover the uncertainty for the original parameters of the boundingboxes which can help to provide probabilistic results for the planning module.

Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles