Datasets for autonomous cars are essential for the development and
benchmarking of perception systems. However, most existing datasets are
captured with camera and LiDAR sensors in good weather conditions. In this
paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to
facilitate research on object detection, tracking and scene understanding using
radar sensing for safe autonomous driving. RADIATE includes 3 hours of
annotated radar images with more than 200K labelled road actors in total, on
average about 4.6 instances per radar image. It covers 8 different categories
of actors in a variety of weather conditions (e.g., sun, night, rain, fog and
snow) and driving scenarios (e.g., parked, urban, motorway and suburban),
representing different levels of challenge. To the best of our knowledge, this
is the first public radar dataset which provides high-resolution radar images
on public roads with a large amount of road actors labelled. The data collected
in adverse weather, e.g., fog and snowfall, is unique. Some baseline results of
radar based object detection and recognition are given to show that the use of
radar data is promising for automotive applications in bad weather, where
vision and LiDAR can fail. RADIATE also has stereo images, 32-channel LiDAR and
GPS data, directed at other applications such as sensor fusion, localisation
and mapping. The public dataset can be accessed at
this http URL

本文介绍了一种名为 RADIATE 的公开数据集，使用雷达传感器获取高分辨率的公共道路数据图像，以进行安全自动驾驶的物体检测，跟踪和场景理解的研究。该数据集是第一个在恶劣天气条件下提供公共道路高分辨率雷达图像的数据集，包含超过 200,000 个标记的道路演员。