Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets

2026-06-30 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

This work investigates uncertainty-aware deep learning for Direction of Arrival (DOA) estimation in automotive radar, particularly for closely spaced targets. It compares a circular-statistics-based von Mises (VM) ensemble (ENS) with an evidential deep learning (EDL) framework. The ENS approach, which parameterizes angular predictions by (mu, kappa), provides interpretable uncertainty aligned with directional geometry and enables direct probabilistic integration into association modules via closed-form VM likelihoods. While ENS achieves lower uncertainty under nominal conditions and shows stronger sensitivity to severe perturbations, EDL offers smoother uncertainty variation and slightly improved ranking consistency. The findings highlight a trade-off between geometric consistency and statistical generality in these uncertainty-aware DOA estimation methods.

Key takeaway

For automotive radar engineers developing advanced perception systems, understanding the trade-offs in uncertainty quantification is crucial. If your application prioritizes geometrically consistent angular uncertainty and direct probabilistic integration into tracking, you should explore von Mises ensemble methods. Conversely, if smoother uncertainty variation and ranking consistency are paramount, consider evidential deep learning frameworks for your Direction of Arrival estimation.

Key insights

Von Mises ensembles offer geometrically consistent uncertainty for automotive radar DOA, balancing against statistical generality.

Principles

• Circular statistics enhance angular uncertainty interpretability.
• Uncertainty quantification methods involve trade-offs.
• Probabilistic integration unifies detection-tracking.

Method

The work compares a von Mises ensemble (VM ENS) using (mu, kappa) parameters with an evidential deep learning (EDL) framework based on normal inverse gamma for DOA estimation.

In practice

• Use VM ENS for high geometric consistency.
• Consider EDL for smoother uncertainty profiles.
• Integrate VM likelihoods for unified pipelines.

Topics

• Automotive Radar
• Direction of Arrival
• Uncertainty Quantification
• Von Mises Ensemble
• Evidential Deep Learning
• Signal Processing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.