Quantification of Uncertainties in Deep Learning-based Environment Perception

• URL: http://arxiv.org/abs/2306.03018v1
• Date: Mon, 5 Jun 2023 16:35:01 GMT
• Title: Quantification of Uncertainties in Deep Learning-based Environment Perception
• Authors: Marco Braun, Moritz Luszek, Jan Siegemund, Kevin Kollek, Anton Kummert
• Abstract summary: We introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans. Our algorithm is capable of differentiating uncertainties in its predictions as being related to an inadequate model. We prove that uncertainties in the model output correlate with the precision of its predictions.
• Score: 0.7874708385247353
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions. The environment of the host vehicle is segmented into equally sized grid cells which are classified individually. Complementary to the segmentation output, our Deep Learning-based algorithm is capable of differentiating uncertainties in its predictions as being related to an inadequate model (epistemic uncertainty) or noisy data (aleatoric uncertainty). To this end, weights are described as probability distributions accounting for uncertainties in the model parameters. Distributions are learned in a supervised fashion using gradient descent. We prove that uncertainties in the model output correlate with the precision of its predictions. Compared to previous concepts, we show superior performance of our approach to reliably perceive the environment of a vehicle.

Related papers

• Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications [0.32302664881848275]
  Evidential deep learning is a technique that extends parametric deep learning to higher-order distributions. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. We show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty.
  arXiv  Detail & Related papers  (2023-09-22T23:04:51Z)
• Conformalized Multimodal Uncertainty Regression and Reasoning [0.9205582989348333]
  This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds. We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries can result in multimodal uncertainties.
  arXiv  Detail & Related papers  (2023-09-20T02:40:59Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Prediction under Latent Subgroup Shifts with High-Dimensional Observations [30.433078066683848]
  We introduce a new approach to prediction in graphical models with latent-shift adaptation. Our novel form of RPM identifies causal latent structure in the source environment, and adapts properly to predict in the target.
  arXiv  Detail & Related papers  (2023-06-23T12:26:24Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)
• DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
  Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner. We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
  arXiv  Detail & Related papers  (2021-02-16T23:50:35Z)
• Probabilistic Deep Learning for Instance Segmentation [9.62543698736491]
  We propose a generic method to obtain model-inherent uncertainty estimates within proposal-free instance segmentation models. We evaluate our method on the BBBC010 C. elegans dataset, where it yields competitive performance.
  arXiv  Detail & Related papers  (2020-08-24T19:51:48Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.