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

TL;DR

The A9-Dataset provides high-resolution, multi-modal sensor data from roadside infrastructure, enabling advanced research in mobility solutions like traffic management and automation with real-world annotated data.

Contribution

This paper introduces the A9-Dataset, a comprehensive multi-sensor dataset from roadside infrastructure, filling a gap in real-world data for mobility research.

Findings

Over 1000 sensor frames and 14,000 traffic objects labeled.

High-resolution multi-modal data from roadside sensors.

Dataset supports development of traffic management and automation systems.

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.