Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies. Acquiring situational awareness using multimodal data collected at high sampling rates by various sensing devices including cameras, LiDAR, and radar requires considerable power, memory and compute resources which are often limited at an edge device. In this paper, we present a novel adaptive radar sub-sampling algorithm designed to identify regions that require more detailed/accurate reconstruction based on prior environmental conditions' knowledge, enabling near-optimal performance at considerably lower effective sampling rates. Designed to robustly perform under variable weather conditions, the algorithm was shown on the Oxford raw radar and RADIATE dataset to achieve accurate reconstruction utilizing only 10% of the original samples in good weather and 20% in extreme (snow, fog) weather conditions. A further modification of the algorithm incorporates object motion to enable reliable identification of important regions. This includes monitoring possible future occlusions caused by objects detected in the present frame. Finally, we train a YOLO network on the RADIATE dataset to perform object detection directly on RADAR data and obtain a 6.6% AP50 improvement over the baseline Faster R-CNN network.

翻译：汽车雷达因自动驾驶技术的日益普及而受到广泛关注。利用摄像头、激光雷达和雷达等传感设备以高采样率采集多模态数据来获取环境态势感知，需要消耗大量电力、内存和计算资源，而这些资源在边缘设备上往往有限。本文提出一种新颖的自适应雷达子采样算法，该算法基于先验环境条件知识，识别需要更精细/精确重建的区域，从而在显著降低有效采样率的情况下实现接近最优的性能。该算法设计为能在多变天气条件下鲁棒运行，并在牛津原始雷达数据集和RADIATE数据集上验证：在良好天气下仅需原始样本的10%即可实现精确重建，在极端天气（雪、雾）条件下也仅需20%。算法的进一步改进融合了物体运动信息，以实现对重要区域的可靠识别，包括监测当前帧检测到的物体可能造成的未来遮挡。最后，我们在RADIATE数据集上训练YOLO网络，直接对雷达数据进行目标检测，相比基线Faster R-CNN网络获得了6.6%的AP50提升。