Hungarian–Austrian EUREKA Central System Project Concludes with Final Event at BME

A four-year research and development project has come to a successful close at the Budapest University of Technology and Economics (BME). Led by BME’s Department of Automotive Technologies, the EUREKA Central System project showcased the next generation of automated vehicles and intelligent road infrastructures. The international consortium — comprising six Hungarian and six Austrian partners — developed cutting-edge technological solutions that open new horizons in testing and developing autonomous mobility systems.

Launched in September 2021, the project presented its final results in October 2025 at BME’s Stoczek Street building. The closing event was opened by Dr. Zsolt Szalay, Head of the Department of Automotive Technologies, followed by presentations from Dr. András Rövid (BME) and Dr. Arno Eichberger (TU Graz), who outlined the project’s main objectives and milestones.

The Next Generation of Smart Roads

At the heart of the project was the development of a centralized system supporting the testing and control of connected and automated vehicles. The system, created by BME’s research team, generates a real-time, high-precision digital twin from roadside sensor data, capable of mapping the movements of both vehicles and their environment.

One of the most striking achievements was the smart road segment built along the shared section of Hungary’s M1–M7 motorways. Based on its digital twin model, BME researchers also developed a mobile application that provides vehicles without onboard sensors with real-time traffic and environmental information — such as lane positioning, surrounding vehicle movements, and static object locations.

“The project’s key significance lies in creating the next generation of smart roads and demonstrating their potential in developing and testing connected and automated vehicles. We also validated our results through real-world demonstrations with our partners,” emphasized Dr. András Rövid, the project’s technical lead and researcher at BME Automated Drive.

Cloud-Based Vehicle Control and Mixed-Reality Testing

Together with experts from TU Wien, STARD, and Virtual Vehicle, the BME team developed cloud-based vehicle control and trajectory planning solutions that enable centralized driving control and teleoperation at speeds of up to 90 km/h.

Another innovation was the mixed-reality testing as a service concept, which allows virtual objects to be integrated into real-world test environments — with vehicles responding to them as if they were real. With the contribution of TU Graz, researchers also implemented real-time integration of actual traffic data into simulation environments, allowing for even more realistic testing conditions.

Extensive Industrial Collaboration

Hungarian project partners included Robert Bosch Kft., Magyar Közút Nonprofit Zrt., Budapest Közút Zrt., Magyar Telekom Nyrt., and Bimfra Kft.
On the Austrian side, participants included TU Graz, TU Wien, Joanneum Research, Tom Robotics, STARD, and Virtual Vehicle.

Magyar Közút was responsible for establishing the sensor infrastructure along the M1–M7 motorway section. Bosch defined the system requirements, validated the smart road using drone-based methods, and integrated advanced driver assistance functions into the central control system. Magyar Telekom coordinated the development of the 5G V2X communication infrastructure, while Budapest Közút and Bimfra conducted high-resolution mapping and 3D modeling tasks.

Collaboration for the Future of Autonomous Mobility

Participants unanimously emphasized that the project not only delivered technological innovations but also laid the groundwork for the future of road infrastructure and the integrated development model of connected autonomous mobility.

The EUREKA Central System project has demonstrated that international, industry–academia collaborations can deliver practical, working solutions to the challenges of intelligent transportation — opening a new chapter in the research and development of automated vehicles.