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

• URL: http://arxiv.org/abs/2604.04490v1
• Date: Mon, 06 Apr 2026 07:30:45 GMT
• Title: RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation
• Abstract summary: RAVEN is a computationally efficient deep learning architecture for FMCW radar perception.<n>Across automotive radar benchmarks, the approach reports strong object detection and BEV free-space segmentation performance.
• Score: 11.345174599707859
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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