论文标题
Carrada数据集：带有范围内的摄像机和汽车雷达注释
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
论文作者
论文摘要
高质量的感知对于自动驾驶（AD）系统至关重要。为了达到此类系统所需的准确性和鲁棒性，必须组合几种类型的传感器。目前，部署了大多数相机和激光扫描仪（LIDAR）以建立车辆周围世界的代表。尽管雷达传感器在汽车行业已经使用了很长时间了，但尽管具有吸引人的特征，但它们仍未使用广告（尤其是它们衡量障碍速度并在不利天气条件下运作的能力）。在很大程度上，这种情况是由于相对缺乏具有原始和注释的真实雷达信号的汽车数据集。在这项工作中，我们介绍了Carrada，这是一个同步摄像机和雷达录音的数据集，并带有range-angle-doppler注释。我们还提出了一种半自动注释方法，该方法用于注释数据集和一个雷达语义分割基线，我们对几个指标进行了评估。我们的代码和数据集都可以在线提供。
High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset are available online.
