Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
This work addresses the need for more reliable and interpretable uncertainty estimates in 3D object detection for autonomous driving systems, representing an incremental improvement.
The paper tackles improving performance and explainable uncertainty in LiDAR-based 3D object detection for autonomous vehicles, achieving up to a 15% increase in Average Precision on the KITTI dataset compared to a baseline using L1 loss.
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.