AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception

• URL: http://arxiv.org/abs/2603.17979v1
• Date: Wed, 18 Mar 2026 17:42:34 GMT
• Title: AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception
• Abstract summary: Radar is a critical perception modality in autonomous driving systems.<n>The sheer volume of high-dimensional raw radar data saturates the communication link to the computing engine.<n>We propose radar data compression with adaptive feedback.
• Score: 26.239044699549506
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Radar is a critical perception modality in autonomous driving systems due to its all-weather characteristics and ability to measure range and Doppler velocity. However, the sheer volume of high-dimensional raw radar data saturates the communication link to the computing engine (e.g., an NPU), which is often a low-bandwidth interface with data rate provisioned only for a few low-resolution range-Doppler frames. A generalized codec for utilizing high-dimensional radar data is notably absent, while existing image-domain approaches are unsuitable, as they typically operate at fixed compression ratios and fail to adapt to varying or adversarial conditions. In light of this, we propose radar data compression with adaptive feedback. It dynamically adjusts the compression ratio by performing gradient descent from the proxy gradient of detection confidence with respect to the compression rate. We employ a zeroth-order gradient approximation as it enables gradient computation even with non-differentiable core operations--pruning and quantization. This also avoids transmitting the gradient tensors over the band-limited link, which, if estimated, would be as large as the original radar data. In addition, we have found that radar feature maps are heavily concentrated on a few frequency components. Thus, we apply the discrete cosine transform to the radar data cubes and selectively prune out the coefficients effectively. We preserve the dynamic range of each radar patch through scaled quantization. Combining those techniques, our proposed online adaptive compression scheme achieves over 100x feature size reduction at minimal performance drop (~1%p). We validate our results on the RADIal, CARRADA, and Radatron datasets.

Related papers

• Dense Soft Weighting for Radar Ego-Velocity Estimation [6.356174487225252]
  We present Dense Soft Weighting, an analytic radar front-end for ego-velocity estimation.<n>It maps every range-Doppler cell to a continuous confidence metric rather than enforcing a binary detection threshold.<n>It reduces mean absolute pose error by 31-45% relative to the strongest CFAR point-cloud baseline.
  arXiv  Detail & Related papers  (2026-07-29T14:37:52Z)
• RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment [15.74169173366249]
  Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM)<n>Most radar SLAM pipelines still rely heavily on frame-to-frame odometry, which leads to substantial drift.<n>We present the first radar bundle adjustment (BA) framework enabled by Gaussian Splatting (GS), a dense and differentiable representation.
  arXiv  Detail & Related papers  (2026-04-15T05:26:46Z)
• RadarGen: Automotive Radar Point Cloud Generation from Cameras [64.69976771710057]
  We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery.<n>RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form.<n>We show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data.
  arXiv  Detail & Related papers  (2025-12-19T18:57:33Z)
• DRO: Doppler-Aware Direct Radar Odometry [11.042292216861762]
  A renaissance in radar-based sensing for mobile robotic applications is underway.<n>We propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars.<n>Our method has been validated on over 250km of on-road data sourced from public datasets.
  arXiv  Detail & Related papers  (2025-04-29T01:20:30Z)
• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion.<n>Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed.<n>Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data [5.344444942640663]
  Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. We introduce an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation.
  arXiv  Detail & Related papers  (2024-06-15T11:26:10Z)
• TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation [21.72892413572166]
  We propose a novel approach to the semantic segmentation of radar scenes using a multi-input fusion of radar data. Our method, TransRadar, outperforms state-of-the-art methods on the CARRADA and RADIal datasets.
  arXiv  Detail & Related papers  (2023-10-03T17:59:05Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.