{
  "id": 406,
  "url": "https://arxiv.org/abs/2606.31473v1",
  "title": "Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets",
  "summary": "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",
  "authors": "Vinay Kulkarni, V. V. Reddy",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T10:49:45.000Z",
  "fetched_at": "2026-07-14T14:14:32.644Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/406",
  "original_url": "https://arxiv.org/abs/2606.31473v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}