A Feasibility Study of a Traffic Supervision System Based on 5G Communication

Allan Tengg*, Michael Stolz, Joachim Hillebrand

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.
Original languageEnglish
Article number6798
Number of pages14
JournalSensors
Volume22
Issue number18
DOIs
Publication statusPublished - 8 Sep 2022

Keywords

  • automated driving
  • 5G networks
  • traffic simulation
  • SUMO

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