  Data-intensive machine learning based techniques increasingly play a
prominent role in the development of future mobility solutions - from driver
assistance and automation functions in vehicles, to real-time traffic
management systems realized through dedicated infrastructure. The availability
of high quality real-world data is often an important prerequisite for the
development and reliable deployment of such systems in large scale. Towards
this endeavour, we present the A9-Dataset based on roadside sensor
infrastructure from the 3 km long Providentia++ test field near Munich in
Germany. The dataset includes anonymized and precision-timestamped multi-modal
sensor and object data in high resolution, covering a variety of traffic
situations. As part of the first set of data, which we describe in this paper,
we provide camera and LiDAR frames from two overhead gantry bridges on the A9
autobahn with the corresponding objects labeled with 3D bounding boxes. The
first set includes in total more than 1000 sensor frames and 14000 traffic
objects. The dataset is available for download at https://a9-dataset.com.
