Robust detection and tracking of objects is crucial for the deployment of
autonomous vehicle technology. Image based benchmark datasets have driven
development in computer vision tasks such as object detection, tracking and
segmentation of agents in the environment. Most autonomous vehicles, however,
carry a combination of cameras and range sensors such as lidar and radar. As
machine learning based methods for detection and tracking become more
prevalent, there is a need to train and evaluate such methods on datasets
containing range sensor data along with images. In this work we present
nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous
vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree
field of view. nuScenes comprises 1000 scenes, each 20s long and fully
annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as
many annotations and 100x as many images as the pioneering KITTI dataset. We
define novel 3D detection and tracking metrics. We also provide careful dataset
analysis as well as baselines for lidar and image based detection and tracking.
Data, development kit and more information are available online.

本文介绍了 nuTonomy 场景数据集，其中包含了自动驾驶车辆所有的传感器：6 个相机，5 个雷达和 1 个激光雷达，每个场景长达 20 秒，并标注了 23 个类别和 8 个属性的三维边界框。同时，定义了新的 3D 检测和跟踪指标，并提供了激光雷达和基于图像的检测和跟踪的基线。