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

• URL: http://arxiv.org/abs/2006.12015v2
• Date: Mon, 17 Aug 2020 21:37:14 GMT
• Title: Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
• Authors: Hujie Pan, Zining Wang, Wei Zhan, Masayoshi Tomizuka
• Abstract summary: We propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection. 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.
• Score: 33.0319422469465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning [0.19418036471925312]
  We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning. Our framework enhances the flexibility of error distribution modeling by incorporating additional higher-order moment, particularly kurtosis.
  arXiv  Detail & Related papers  (2024-08-05T08:12:25Z)
• Toward Reliable Human Pose Forecasting with Uncertainty [51.628234388046195]
  We develop an open-source library for human pose forecasting, including multiple models, supporting several datasets. We devise two types of uncertainty in the problem to increase performance and convey better trust.
  arXiv  Detail & Related papers  (2023-04-13T17:56:08Z)
• Model-Based Uncertainty in Value Functions [89.31922008981735]
  We focus on characterizing the variance over values induced by a distribution over MDPs. Previous work upper bounds the posterior variance over values by solving a so-called uncertainty Bellman equation. We propose a new uncertainty Bellman equation whose solution converges to the true posterior variance over values.
  arXiv  Detail & Related papers  (2023-02-24T09:18:27Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• On Calibrated Model Uncertainty in Deep Learning [0.0]
  We extend the approximate inference for the loss-calibrated Bayesian framework to dropweights based Bayesian neural networks. We show that decisions informed by loss-calibrated uncertainty can improve diagnostic performance to a greater extent than straightforward alternatives.
  arXiv  Detail & Related papers  (2022-06-15T20:16:32Z)
• Robust Depth Completion with Uncertainty-Driven Loss Functions [60.9237639890582]
  We introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion. Our method has been tested on KITTI Depth Completion Benchmark and achieved the state-of-the-art robustness performance in terms of MAE, IMAE, and IRMSE metrics.
  arXiv  Detail & Related papers  (2021-12-15T05:22:34Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation [25.003116148843525]
  Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. We present a novel decoder framework where pixel-wise perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty.
  arXiv  Detail & Related papers  (2021-09-20T23:30:04Z)
• Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty [12.531126969367774]
  We leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters. Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple uncertaintys by up to 3.6% in terms of Average Precision.
  arXiv  Detail & Related papers  (2020-08-10T14:49:49Z)
• Localization Uncertainty Estimation for Anchor-Free Object Detection [48.931731695431374]
  There are several limitations of the existing uncertainty estimation methods for anchor-based object detection. We propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets that are homogeneous, so that it can tell which direction is uncertain.
  arXiv  Detail & Related papers  (2020-06-28T13:49:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.