Sensors (Mar 2021)

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

  • Viktor Tihanyi,
  • Tamás Tettamanti,
  • Mihály Csonthó,
  • Arno Eichberger,
  • Dániel Ficzere,
  • Kálmán Gangel,
  • Leander B. Hörmann,
  • Maria A. Klaffenböck,
  • Christoph Knauder,
  • Patrick Luley,
  • Zoltán Ferenc Magosi,
  • Gábor Magyar,
  • Huba Németh,
  • Jakob Reckenzaun,
  • Viktor Remeli,
  • András Rövid,
  • Matthias Ruether,
  • Selim Solmaz,
  • Zoltán Somogyi,
  • Gábor Soós,
  • Dávid Szántay,
  • Tamás Attila Tomaschek,
  • Pál Varga,
  • Zsolt Vincze,
  • Christoph Wellershaus,
  • Zsolt Szalay

DOI
https://doi.org/10.3390/s21062169
Journal volume & issue
Vol. 21, no. 6
p. 2169

Abstract

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A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

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