A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
This dataset addresses the data scarcity problem for researchers and developers working on machine learning applications in mobility, such as driver assistance and traffic management, though it is incremental as it builds on existing infrastructure-based data collection efforts.
The authors tackled the need for high-quality real-world data for mobility research by introducing the A9-Dataset, a multi-sensor infrastructure-based dataset from a 3 km test field in Germany, which includes over 1000 sensor frames and 14000 labeled traffic objects.
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.