论文标题
汽车雷达中面向概率的对象检测
Probabilistic Oriented Object Detection in Automotive Radar
论文作者
论文摘要
自主雷达是高级驾驶员辅助系统不可或缺的一部分，因为它对不利天气和各种照明条件的稳健性。常规的汽车雷达使用数字信号处理（DSP）算法将原始数据处理为稀疏的雷达引脚，这些数据不提供有关对象的大小和方向的信息。在本文中，我们提出了一种基于学习的算法，用于雷达对象检测。该算法在其原始张量表示中填充了雷达数据，并将概率面向的边界框放在检测到的对象围绕鸟类视图的空间中。我们创建了一个具有102544帧的原始雷达和同步激光雷达数据的新的多模式数据集。为了减少人类注释的工作，我们开发了可扩展的管道，以使用LIDAR作为参考自动注释地面真相。基于此数据集，我们使用原始雷达数据作为唯一的输入开发了车辆检测管道。我们最佳性能的雷达检测模型在0.3的定向下达到77.28 \％ap。据我们所知，这是第一次尝试使用原始雷达数据进行传统角雷达的原始雷达数据研究对象检测。
Autonomous radar has been an integral part of advanced driver assistance systems due to its robustness to adverse weather and various lighting conditions. Conventional automotive radars use digital signal processing (DSP) algorithms to process raw data into sparse radar pins that do not provide information regarding the size and orientation of the objects. In this paper, we propose a deep-learning based algorithm for radar object detection. The algorithm takes in radar data in its raw tensor representation and places probabilistic oriented bounding boxes around the detected objects in bird's-eye-view space. We created a new multimodal dataset with 102544 frames of raw radar and synchronized LiDAR data. To reduce human annotation effort we developed a scalable pipeline to automatically annotate ground truth using LiDAR as reference. Based on this dataset we developed a vehicle detection pipeline using raw radar data as the only input. Our best performing radar detection model achieves 77.28\% AP under oriented IoU of 0.3. To the best of our knowledge, this is the first attempt to investigate object detection with raw radar data for conventional corner automotive radars.
