自動運転車の3D物体検出における性能向上と説明可能な不確実性
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
LiDARベースの3D物体検出向けに、コーナー変換と不確実性モデリングを用いた新しい損失関数を提案し、KITTIデータセットで最大15%の精度向上と、点群密度に整合する解釈可能な不確実性推定を実現した。
著者: Hujie Pan, Zining Wang, Wei Zhan, Masayoshi Tomizuka
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction. The loss function was designed using corner transformation and uncertainty modeling. With the new loss function, the performance 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 using simple L1 Loss. In the study of the characteristics of predicted uncertainties, we find that generally more accurate prediction of the bounding box is usually accompanied by lower uncertainty. The distribution of corner uncertainties agrees on the distribution of the point cloud in the bounding box, which means the corner with denser observed points has lower uncertainty. Moreover, our method also learns the constraint from the cuboid geometry of the bounding box in uncertainty prediction. Finally, we propose an efficient Bayesian updating method to recover the uncertainty for the original parameters of the bounding boxes which can help to provide probabilistic results for the planning module.
