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.
By Vinay Kulkarni, V. V. Reddy
arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.
By Pietro Carlotti, Nevena Gligi\'c, Arya Farahi
arXiv:2607. 21745v1 Announce Type: cross Abstract: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems.
By Khalid El-Darymli, Christoph H. Gierull, Katerina Biron, Weimin Huang
arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).
By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
By H. Martin Gillis, Isaac Xu, Thomas Trappenberg
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.