论文标题
朝着更好的性能和3D对象检测自动驾驶汽车检测的不确定性
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
论文作者
论文摘要
在本文中，我们提出了一种新型的损耗函数形式，以提高基于激光雷达的3D对象检测的性能，并获得对预测的更具解释和令人信服的不确定性。使用角转换和不确定性建模设计损耗函数。随着新的损失函数，我们方法在Kitti数据集的Val拆分上的性能在平均精度（AP）方面与基线相比，使用简单的L1损失提高了15％。在对预测不确定性的特征的研究中，我们发现通常更准确地预测边界框，伴随着较低的不确定性。角不确定性的分布在边界框中的点云的分布一致，这意味着观察到的点具有较密的角度的不确定性较低。此外，我们的方法还从不确定性预测中的边界框的立方几何形状中学习了约束。最后，我们提出了一种有效的贝叶斯更新方法，以恢复边界框的原始参数的不确定性，这可以帮助为计划模块提供概率结果。
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.
