A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research

Abstract

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.

Automatic Label Injection Into Local Infrastructure LiDAR Point Cloud for Training Data Set Generation

“…In [12] the different representation of objects in point clouds acquired by different types of LiDAR sensors is handled by training a neural network to increase the density of points along the surface of objects. A recently presented unique infrastructure data set [13] on the other hand, enables to train machine learning-based object detectors to detect objects from a specific (higher) viewpoint. Creating sensor and mounting position specific training datasets for infrastructure LiDAR sensors manually requires significant time and effort, nevertheless in case of manual labeling there might be relevant variance in the quality of labels.…”