InTraSafEd 5G project - Increasing Traffic Safety with Edge and 5G

Despite advances in vehicle technology and road modernization, traffic accidents are a huge global issue, causing deaths and injuries, especially among pedestrians and cyclists. This often happens due to pedestrians and cyclists in drivers’ blind spots or distractions delaying drivers’ reactions. For these reasons, timely notification of drivers about critical situations is important to increase traffic safety. Recently, new edge computing and communication technologies have been proposed to reduce latency in critical IoT systems. However, state-of-the-art solutions either do not focus on traffic safety or do not consider low-latency requirements in this context.

We propose InTraSafEd5G (Increasing Traffic Safety with Edge and 5G) to address this issue. InTraSafEd5G performs real-time edge analytics to detect critical situations and deliver early warning notifications to drivers. After showing a detailed analysis of the design choices, we provide a prototype implementation of the system and evaluate its performance in a real-world setup. The evaluation shows that InTraSafEd5G is able to (i) detect critical situations on the chosen intersection in real-time and (ii) notify affected drivers in 108.73𝑚𝑠 on average using 5G, which is within expected latency requirements of road safety IoT applications. Our solution shows a promising step towards increasing overall traffic safety and supporting decision-making in critical situations.


5G Use Case Challenge City of Vienna

Team members
Related Publications
  • Ivan Lujic, Vincenzo De Maio, Klaus Pollhammer, Ivan Bodrozic, Josip Lasic and Ivona Brandic, "Increasing Traffic Safety with Real-Time Edge Analytics and 5G," 4th ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2021), Analytics and Networking, 2021. (to appear)
  • Atakan Aral, Vincenzo De Maio and Ivona Brandic, "ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks," IEEE Transactions on Sustainable Computing, (accepted for publication).
  • Atakan Aral and Ivona Brandic, "Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services," IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, pp. 1578-1590, July 2021.
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