Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets

• URL: http://arxiv.org/abs/2606.31473v1
• Date: Tue, 30 Jun 2026 10:49:45 GMT
• Title: Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets
• Abstract summary: This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar.<n>A circular-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation.<n>ENS achieves lower uncertainty under nominal conditions and exhibits stronger sensitivity to severe perturbations, whereas EDL provides smoother uncertainty variation and slightly improved ranking consistency.
• Score: 0.5378499388029053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabilistic modeling and downstream integration. A circular-statistics-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation, yielding a Student t predictive distribution in the Euclidean domain. The ENS framework produces angular predictions parameterized by (mu, kappa), enabling interpretable uncertainty aligned with directional geometry. Performance is evaluated under in distribution and multiple out-of-distribution conditions using risk coverage and ROC or AUROC analyses. Results indicate that ENS achieves lower uncertainty under nominal conditions and exhibits stronger sensitivity to severe perturbations, whereas EDL provides smoother uncertainty variation and slightly improved ranking consistency. Importantly, the ENS representation enables direct probabilistic integration into association modules via closed form VM likelihoods, facilitating a unified detection tracking pipeline. These findings highlight a trade-off between geometric consistency and statistical generality in uncertainty-aware DOA estimation.

Related papers

• Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis [14.31446990918297]
  We present a novel pipeline for vision-based traversability estimation that explicitly models contextual uncertainty.<n>Our approach utilizes Conceptual Anchoring to ground open-vocabulary VLM predictions onto a continuous physical traversability scale.<n>We derive both a dense traversability map and a dense uncertainty map based on the statistical properties of the distribution.
  arXiv  Detail & Related papers  (2026-09-08T11:18:32Z)
• Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression [7.011984503405774]
  Deep Imbalanced Regression is pervasive in continuous prediction tasks across diverse modalities.<n>We propose DUO, an uncertainty-aware long-tailed regression framework.<n>We show that DUO achieves the best few-shot bMAE and GM on IMDB-WIKI-DIR, AgeDB-DIR, and AAV2-DIR while remaining competitive on few-shot MAE.
  arXiv  Detail & Related papers  (2026-09-04T11:06:57Z)
• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• DCS: A Unified Conditional Sensitivity Framework for Cross-Modal Copyright Infringement Detection [0.0]
  We develop a unified post-hoc detection framework that treats copyright infringement evidence as a counterfactual conditional distribution shift.<n>A protected target is suspicious when the model's behavior under aligned conditions would change measurably if that target were included in, or removed from, the training process.<n>The framework is instantiated for ridge-regularized linear regression, conditional diffusion models, autoregressive language models, and multimodal models.
  arXiv  Detail & Related papers  (2026-07-24T07:00:44Z)
• Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation [55.47411235954028]
  We recast imputation under missing completely at random as density estimation from masked observations.<n>We obtain a convex empirical risk problem with closed form marginals, solvable by a Newton interior point method.<n>The resulting PSD Impute model yields both single and multiple imputations from the same fitted density.
  arXiv  Detail & Related papers  (2026-07-08T15:51:05Z)
• Equivariant Filter for Radar-Inertial Odometry [45.85581416235741]
  Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU)<n>This letter proposes an Equivariant Filter (EqF) for RIO based on a Lie group symmetry that geometrically couples navigation states and IMU biases.
  arXiv  Detail & Related papers  (2026-04-24T21:47:06Z)
• Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation [0.6340400318304492]
  Variance-Gated Ensembles (VGE) is an intuitive framework that injects epistemic sensitivity via a signal-to-noise gate computed from ensemble statistics.<n>We derive closed-form vector-Jacobian products enabling end-to-end training through ensemble sample mean and variance.
  arXiv  Detail & Related papers  (2026-02-08T22:05:23Z)
• Km-scale dynamical downscaling through conformalized latent diffusion models [45.94979929172337]
  Dynamical downscaling is crucial for deriving high-resolution meteorological fields from coarse-scale simulations.<n>Generative Diffusion models (DMs) have recently emerged as powerful data-driven tools for this task.<n>However, DMs lack finite-sample guarantees against overconfident predictions, resulting in miscalibrated grid-point-level uncertainty estimates.<n>We tackle this issue by augmenting the downscaling pipeline with a conformal prediction framework.
  arXiv  Detail & Related papers  (2025-10-15T08:41:36Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI [33.7054351451505]
  We propose a probabilistic deep learning framework based on Deep Ensembles of Mixture Density Networks (MDNs)<n>MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient D and fraction f parameters, although slight overconfidence was observed in pseudo-diffusion coefficient D*.<n>We present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates.
  arXiv  Detail & Related papers  (2025-08-06T16:08:55Z)
• An Uncertainty-aware DETR Enhancement Framework for Object Detection [10.102900613370817]
  We propose an uncertainty-aware enhancement framework for DETR-based object detectors.<n>We derive a Bayes Risk formulation to filter high-risk information and improve detection reliability.<n> Experiments on the COCO benchmark show that our method can be effectively integrated into existing DETR variants.
  arXiv  Detail & Related papers  (2025-07-20T07:53:04Z)
• Similarity-Distance-Magnitude Universal Verification [0.0]
  We build SDM networks with uncertainty-aware verification and interpretability-by-exemplar as intrinsic properties.<n>We provide open-source software implementing these results.
  arXiv  Detail & Related papers  (2025-02-27T15:05:00Z)
• Revisiting Essential and Nonessential Settings of Evidential Deep Learning [70.82728812001807]
  Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation. We propose Re-EDL, a simplified yet more effective variant of EDL.
  arXiv  Detail & Related papers  (2024-10-01T04:27:07Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)
• Distribution Regression with Sliced Wasserstein Kernels [45.916342378789174]
  We propose the first OT-based estimator for distribution regression. We study the theoretical properties of a kernel ridge regression estimator based on such representation.
  arXiv  Detail & Related papers  (2022-02-08T15:21:56Z)

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.