The AI4Mobile project, led by the Fraunhofer Heinrich-Hertz-Institut (HHI), has concluded with successful results the project, which began in 2020, aimed to develop AI-based communication solutions for mobility applications in industry and transportation. It was funded by the German Federal Ministry of Education and Research (BMBF) with 5.1 million euros as part of the high-tech strategy of the German federal government in the field of “Artificial Intelligence in Communication Networks.”
AI4Mobile focused on modernizing mobile communication systems and networks for future use. The researchers designed innovative artificial intelligence and machine learning methods that enable the prediction of quality of service (QoS) for high mobility, proactive resource optimization, and networking in all parts of the mobile network infrastructure.
The project identified various use cases from the fields of transportation and industry and analyzed them in terms of their requirements. These use cases included teleoperated driving and cooperation between mobile and stationary robots. To generate data and evaluate the concepts, the team took a three-stage approach consisting of the virtual replication of the systems, private campus networks in laboratory and factory environments, and extensive test and measurement campaigns in public scenarios or mobile networks.
The datasets generated in the project included information from all parts of the cellular infrastructure, vehicle-specific information such as route, position, and speed, data from high-quality sensors like LIDAR, and publicly available contextual information like traffic and weather data. The consortium has published a large part of the datasets obtained during the measurement campaigns as well as the software for the complete data processing chain. This move is seen as a significant contribution to the research and development of AI-based algorithms for mobile applications by research groups worldwide.
The developed solutions will support novel mobility applications in passenger transport, freight transport, and industrial production environments. Moreover, the predicted QoS information is used to develop AI mechanisms for real-time optimization and dynamic adaptation in all parts of the mobile networks.
The Berlin V2X dataset provides high-resolution GPS-located wireless measurements in various urban environments in Berlin for both cellular and sidelink radio technologies. The data was collected using four vehicles over three days. The AI4Mobile iV2V and iV2I+ Industrial Wireless datasets contain wireless measurements from two industrial test environments: iV2V (industrial vehicle-to-vehicle) and iV2I+ (industrial vehicle-to-infrastructure plus sensor). The data includes information on physical layer parameters (e.g., signal strength and signal quality), position data as well as wireless QoS such as delay and throughput. The datasets are annotated and pre-filtered for fast onboarding and applicability.
The AI4Mobile project is seen as a significant step forward in modernizing mobile communication systems and networks, and it is expected to have a significant impact on the development of AI-based communication solutions for mobility applications. It is seen as an important contribution to the research and development of AI-based algorithms for mobile applications by research groups worldwide.
The project involved several partners, including BMW AG, Robert Bosch GmbH, Enway GmbH, Ericsson GmbH, Götting KG, RFmondial GmbH, TU Dresden (Vodafone Chair for Mobile Radio Systems and Deutsche Telekom Chair for Communication Networks), RPTU Kaiserslautern (Chair for Radio Communication and Navigation), and Fraunhofer HHI. The success of the AI4Mobile project reinforces the notion that machine learning will be an important element in the evolution of mobile networks.
Dr. Frank Hofmann, Chief Expert for Communication Systems at Bosch Research, reports, “Thanks to the work with our partners in this project, we have achieved important results for the use of AI-based communication solutions. One example is the prediction of connection quality for Connected Mobility and Industry 4.0.”