让雷达自己学会怎么采样，用更少天线实现更好感知。
DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception
- 端到端训练雷达的稀疏采集模式，联合优化感知与融合。
- 在RADIal数据集上用更少接收天线达到或超过全阵列性能。
- 适合关注低成本高效率自动驾驶感知系统的研发者。
DeeperRadar 是一种以雷达为中心、依赖传感器堆栈条件的框架，通过端到端学习稀疏采集模式，协同设计雷达感知与多模态3D检测。一个可学习的MIMO设计模块在融合网络中与原始雷达ADC数据、相机图像和LiDAR点云共同训练。训练时，该模块由其他传感器监督，使系统学会激活哪些接收天线及有效数量。部署时，设计模块被替换为学到的稀疏子采样掩码，下游模型结构不变。在RADIal数据集上的评估表明，DeeperRadar发现的稀疏任务感知雷达配置在使用更少接收天线的情况下，性能可匹配或超越全阵列基线，有望降低雷达成本与集成复杂度。结果说明，学习到的最优MIMO雷达设计依赖于融合堆栈和下游感知任务。
原文摘要 · Abstract (English)
DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model. A learnable MIMO design module is trained end-to-end within a fusion network that operates directly on raw radar ADC data together with camera images and LiDAR point clouds. During training, the design module is supervised by the other sensors, enabling the system to learn both which receiver antennas to activate and the effective number of them. At deployment, the design module is removed and replaced by the learned sparse subsampling mask, leaving the downstream model architecture unchanged. Evaluated on the RADIal dataset, DeeperRadar discovers sparse, task-aware radar configurations that match or exceed full-array baselines while using fewer receivers, potentially reducing radar cost and integration complexity. These results show that learned optimal MIMO radar design depends on the fusion stack and the downstream perception task.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。