arXiv:2606.31473v1 Announce Type: cross 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
The increasing complexity and safety demands of autonomous systems, particularly in automotive radar, necessitate more robust and interpretable uncertainty quantification in deep learning models.
Advanced uncertainty quantification in AI, as demonstrated for automotive radar, is critical for building trustworthy autonomous systems and for regulatory acceptance and broad deployment of AI-powered solutions.
The ability to accurately and interpretably quantify uncertainty in AI predictions, such as direction of arrival for radar, will improve the reliability and decision-making capabilities of autonomous vehicles.
- · Automotive AI developers
- · Autonomous vehicle manufacturers
- · Insurance companies
- · ADAS suppliers
- · Developers of less robust AI models
- · Companies reliant on rules-based automotive safety
Improved safety and reliability of autonomous driving systems due to better understanding of AI's predictive confidence.
Faster adoption and regulatory approval of autonomous vehicles as concerns about AI explainability and safety are addressed.
The development of new safety standards and testing methodologies specifically focused on uncertainty-aware AI algorithms in critical applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI