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：由于对自主驱动技术的兴趣日益提高,汽车雷达日益引起注意;利用各种遥感装置,包括照相机、激光雷达和雷达等高取样率收集的多式联运数据获得情况认识,需要大量的动力、内存和计算资源,而这些资源往往局限于边缘装置;在本文件中,我们提出了一种新的适应性雷达子抽样算法,目的是根据先前的环境条件知识,查明需要更详细/更精确重建的区域,使在相当低的有效采样率上取得接近最佳的性能;为了在可变天气条件下强有力地进行采样,在牛津原始雷达和RADIATE数据集上显示算法,以便进行准确的重建,只利用10%的原样在良好的天气中,20%在极端的(目前为雾)天气条件下;进一步修改算法,纳入物体运动,以便能够可靠地识别重要区域;这包括监测目前框架中发现的物体可能造成的未来封闭情况;最后,我们在RADAR数据上培训一个YOLO网络,以便直接进行物体探测,并在基线上更快的R-CN网络上获得6.6%的AP50改进。