Automotive radar perception pipelines commonly construct angle-domain representations via beamforming before applying learning-based models. This work instead investigates a representational question: can meaningful spatial structure be learned directly from pre-beamforming per-antenna range-Doppler (RD) measurements? Experiments are conducted on a 6-TX x 8-RX (48 virtual antennas) commodity automotive radar employing an A/B chirp-sequence frequency-modulated continuous-wave (CS-FMCW) transmit scheme, in which the effective transmit aperture varies between chirps (single-TX vs. multi-TX), enabling controlled analysis of chirp-dependent transmit configurations. We operate on pre-beamforming per-antenna RD tensors using a dual-chirp shared-weight encoder trained in an end-to-end, fully data-driven manner, and evaluate spatial recoverability using bird's-eye-view (BEV) occupancy as a geometric probe rather than a performance-driven objective. Supervision is visibility-aware and cross-modal, derived from LiDAR with explicit modeling of the radar field-of-view and occlusion-aware LiDAR observability via ray-based visibility. Through chirp ablations (A-only, B-only, A+B), range-band analysis, and physics-aligned baselines, we assess how transmit configurations affect geometric recoverability. The results indicate that spatial structure can be learned directly from pre-beamforming per-antenna RD tensors without explicit angle-domain construction or hand-crafted signal-processing stages.

翻译：汽车雷达感知管道通常在进行基于学习的模型应用之前，通过波束赋形构建角度域表示。本研究转而探讨一个表征问题：能否直接从预波束赋形的单天线距离-多普勒（RD）测量中学习有意义的空间结构？实验采用6发射×8接收（48个虚拟天线）商用汽车雷达，该雷达采用A/B啁啾序列调频连续波（CS-FMCW）发射方案，其中有效发射孔径在啁啾（单发射与多发射）之间变化，从而实现对啁啾相关发射配置的可控分析。我们使用双啁啾共享权重编码器对预波束赋形的单天线RD张量进行操作，该编码器以端到端、完全数据驱动的方式训练，并通过鸟瞰图（BEV）占用率作为几何探针（而非性能驱动目标）来评估空间可恢复性。监督是可见性感知且跨模态的，源自LiDAR，通过基于射线的可见性显式建模雷达视野和考虑遮挡的LiDAR可观测性。通过啁啾消融（仅A、仅B、A+B）、距离带分析和物理对齐基线，我们评估了发射配置对几何可恢复性的影响。结果表明，无需显式构建角度域或手工设计的信号处理阶段，即可直接从预波束赋形的单天线RD张量中学习空间结构。