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.
@article{arxiv.2604.04490,
title = {RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation},
author = {Anuvab Sen and Mir Sayeed Mohammad and Saibal Mukhopadhyay},
journal= {arXiv preprint arXiv:2604.04490},
year = {2026}
}