Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets
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
Record details
Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Children & education · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (30 June 2026), “Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets,” evidence record 406, https://ethics.ai/record/406 (originally published by arXiv).
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