Intelligent Transportation Systems (ITS) allow a drastic expansion of the visibility range and decrease occlusions for autonomous driving. To obtain accurate detections, detailed labeled sensor data for training is required. Unfortunately, high-quality 3D labels of LiDAR point clouds from the infrastructure perspective of an intersection are still rare. Therefore, we provide the A9 Intersection Dataset, which consists of labeled LiDAR point clouds and synchronized camera images. Here, we recorded the sensor output from two roadside cameras and LiDARs mounted on intersection gantry bridges. The point clouds were labeled in 3D by experienced annotators. Furthermore, we provide calibration data between all sensors, which allow the projection of the 3D labels into the camera images and an accurate data fusion. Our dataset consists of 4.8k images and point clouds with more than 57.4k manually labeled 3D boxes. With ten object classes, it has a high diversity of road users in complex driving maneuvers, such as left and right turns, overtaking, and U-turns. In experiments, we provided multiple baselines for the perception tasks. Overall, our dataset is a valuable contribution to the scientific community to perform complex 3D camera-LiDAR roadside perception tasks. Find data, code, and more information at https://a9-dataset.com.

翻译：智能交通系统(ITS)能够显著扩展自动驾驶的视野范围并减少遮挡。为获得精确的检测结果，需要带有详细标签的训练传感器数据。然而，目前从交叉路口基础设施视角获取的高质量3D激光雷达点云标签仍然稀缺。为此，我们提供了A9交叉路口数据集，其中包含已标注的激光雷达点云和同步相机图像。我们记录了两个路边摄像头和安装在交叉路口龙门架上的激光雷达的传感器输出。点云由经验丰富的标注员进行3D标注。此外，我们提供了所有传感器之间的标定数据，这可将3D标签投影到相机图像中，并实现精确的数据融合。本数据集包含4.8万张图像和点云，其中超过5.74万个手动标注的3D边界框。数据集涵盖十类物体，在复杂驾驶场景（如左转、右转、超车及掉头）中具有高度的道路使用者多样性。实验中，我们为感知任务提供了多个基线方法。总体而言，本数据集为学术界执行复杂的3D相机-激光雷达道路周边感知任务提供了宝贵资源。数据、代码及更多信息请访问 https://a9-dataset.com。