  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.
