RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914448230318080 |
|---|---|
| author | Sen, Anuvab Mohammad, Mir Sayeed Mukhopadhyay, Saibal |
| author_facet | Sen, Anuvab Mohammad, Mir Sayeed Mukhopadhyay, Saibal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04490 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation Sen, Anuvab Mohammad, Mir Sayeed Mukhopadhyay, Saibal Signal Processing Artificial Intelligence Image and Video Processing 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. |
| title | RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation |
| topic | Signal Processing Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2604.04490 |