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.

翻译：雷达因其全天候特性及测量距离与多普勒速度的能力，成为自动驾驶系统中的关键感知模态。然而，高维原始雷达数据的数据量巨大，会饱和通往计算引擎（如NPU）的通信链路——该接口通常带宽较低，其数据速率仅能支持少量低分辨率距离-多普勒帧的传输。目前尚缺乏一种适用于高维雷达数据的通用编解码器，而现有的图像域方法因通常在固定压缩比下工作且无法适应变化或对抗性条件而不适用。为此，我们提出具有自适应反馈的雷达数据压缩方法。该方法通过执行检测置信度相对于压缩率的代理梯度下降，动态调整压缩比。我们采用零阶梯度近似，因其即使面对非可微核心操作（剪枝与量化）也能实现梯度计算。这还避免了在带宽受限链路上传输梯度张量——若进行估算，该张量大小将与原始雷达数据相当。此外，我们发现雷达特征图高度集中于少量频率分量。因此，我们对雷达数据立方体应用离散余弦变换，并有效选择性地剪除系数。通过缩放量化保留每个雷达分块的动态范围。结合上述技术，我们提出的在线自适应压缩方案在性能下降极小的条件下（约1个百分点）实现了超过100倍的特征尺寸缩减。我们在RADIal、CARRADA和Radatron数据集上验证了结果。