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

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.Comment: Accepted for IEEE Intelligent Vehicles Symposium 2022 (IV22

AUTOtech.agil: architecture and technologies for orchestrating automotive agility

Future mobility will be electrified, connected and automated. This opens completely new possibilities for mobility concepts that have the chance to improve not only the quality of life but also road safety for everyone. To achieve this, a transformation of the transportation system as we know it today is necessary. The UNICARagil project, which ran from 2018 to 2023, has produced architectures for driverless vehicles that were demonstrated in four full-scale automated vehicle prototypes for different applications. The AUTOtech.agil project builds upon these results and extends the system boundaries from the vehicles to include the whole intelligent transport system (ITS) comprising, e.g., roadside units, coordinating instances and cloud backends. The consortium was extended mainly by industry partners, including OEMs and tier 1 suppliers with the goal to synchronize the concepts developed in the university-driven UNICARagil project with the automotive industry. Three significant use cases of future mobility motivate the consortium to develop a vision for a Cooperative Intelligent Transport System (C-ITS), in which entities are highly connected and continually learning. The proposed software ecosystem is the foundation for the complex software engineering task that is required to realize such a system. Embedded in this ecosystem, a modular kit of robust service-oriented modules along the effect chain of vehicle automation as well as cooperative and collective functions are developed. The modules shall be deployed in a service-oriented E/E platform. In AUTOtech.agil, standardized interfaces and development tools for such platforms are developed. Additionally, the project focuses on continuous uncertainty consideration expressed as quality vectors. A consistent safety and security concept shall pave the way for the homologation of the researched ITS