论文标题
从子采样雷达数据中检测对象检测的端到端系统
End-to-end system for object detection from sub-sampled radar data
论文作者
论文摘要
强大而准确的感知对于推进自动驾驶汽车系统至关重要。使用雷达等传感器在复杂的城市条件下获取情境意识的需求激发了对功率和延迟有效信号获取方法的研究。在本文中，我们提出了能够在极端天气条件下运行的端到端信号处理管道，该管道依赖于子采样的雷达数据来在车辆设置中执行对象检测。对象检测的结果进一步用于子样本即将到来的雷达数据，该数据与以前的工作相反，该工作依赖于图像信息。我们根据极端天气条件（例如雪或雾）以及在低鲜明的夜晚在极端天气条件下使用20％的样品重建的雷达数据显示了可靠的检测。此外，我们在微调集合中生成20％的采样雷达数据，并在场景中显示AP50的1.1％增长，在高速公路状况下AP50增益3％。
Robust and accurate sensing is of critical importance for advancing autonomous automotive systems. The need to acquire situational awareness in complex urban conditions using sensors such as radar has motivated research on power and latency-efficient signal acquisition methods. In this paper, we present an end-to-end signal processing pipeline, capable of operating in extreme weather conditions, that relies on sub-sampled radar data to perform object detection in vehicular settings. The results of the object detection are further utilized to sub-sample forthcoming radar data, which stands in contrast to prior work where the sub-sampling relies on image information. We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions such as snow or fog, and on low-illuminated nights. Additionally, we generate 20% sampled radar data in a fine-tuning set and show 1.1% gain in AP50 across scenes and 3% AP50 gain in motorway condition.
