Summary The first true multitechnology communication system is 5G, which is expected to have a large impact on society and industry. The European Commission funded H2020 5G‐PPP Phase 2 project SaT5G addressed the plug‐and‐play integration of satellite communication into 5G. One of the SaT5G use cases corresponds to the delivery of 5G connectivity services to moving platforms such as aircraft via GEO/MEO satellite backhauling. With focus on this use case, this paper elaborates on the practical implementation and measurement results obtained within the 5G Aero testbed developed as part of the SaT5G project. The 5G Aero testbed activities focus on the next generation of connectivity and content distribution services to airplanes through satellite and terrestrial integration in 5G at the user, control and management planes. Software‐defined networking (SDN) and network functions virtualisation (NFV) are key enablers to develop a powerful end‐to‐end testbed that can accelerate the adoption of multi‐access edge computing (MEC) for the next‐generation in‐flight entertainment and connectivity (IFEC) services, which use geostationary (GEO) and medium Earth orbit (MEO) satellite backhauling technologies. Hence, measurement results obtained from both over‐the‐air demonstration over the O3b MEO satellite constellation and in‐lab validation over an emulated GEO satellite link are presented, towards the next‐generation 5G‐enabled IFEC services.

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km h −1 and 28 m s −2 . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.

No abstract

No abstract

No abstract