[2604.04490] RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation

Electrical Engineering and Systems Science > Signal Processing arXiv:2604.04490 (eess) [Submitted on 6 Apr 2026] Title:RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation Authors:Anuvab Sen, Mir Sayeed Mohammad, Saibal Mukhopadhyay View a PDF of the paper titled RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation, by Anuvab Sen and 2 other authors View PDF HTML (experimental) Abstract:This paper presents RAVEN, a computationally efficient deep learning architecture for FMCW radar perception. The method processes raw ADC data in a chirp-wise streaming manner, preserves MIMO structure through independent receiver state-space encoders, and uses a learnable cross-antenna mixing module to recover compact virtual-array features. It also introduces an early-exit mechanism so the model can make decisions using only a subset of chirps when the latent state has stabilized. Across automotive radar benchmarks, the approach reports strong object detection and BEV free-space segmentation performance while substantially reducing computation and end-to-end latency compared with conventional frame-based radar pipelines. Comments: Subjects: Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV) Cite as: arXiv:2604.04490 [eess.SP]   (or arXiv:2604.04490v1 [eess.SP] for this version)   https://doi.org/10.48550/arXiv.2604.04490 Focus to learn more arXiv-issued D...