Project description
5G paves the way for safer, connected autonomous vehicles
The role of connected vehicles in the transportation system is paving the way to improved road safety. Wireless communication is increasing the potential of autonomous vehicles on issues such as collision avoidance and coordination. However, most advanced methods take for granted the wireless link, neglecting the communication commitment and the processing latency. The EU-funded C-AVOID project will build a realistic structure based on the new generation of cellular networks (5G). It presents a proven potential for transportation systems due to low end-to-end latency and edge computational capabilities of the Multi-access Edge Computing (MEC) platform. This structure enables local databases where typical driver manoeuvres can be modelled through machine learning for collision avoidance, as well as a platform that can host algorithms coordinating autonomous vehicles, respecting strict quality of service requirements.
Objective
Connected Vehicles (CVs) represent a potentially disruptive yet beneficial change to our transportation systems. Wireless communication capabilities on vehicles enable a better understanding of the environment complementing on-board sensors and increase the potentials of autonomous vehicles. For this reason, communication among vehicles has always played an important role in safety applications and an extended body of research is present on, e.g. collision avoidance or autonomous vehicles coordination. Almost all state-of-the-art approaches take for granted the wireless link, ignoring the communication load and the processing latency that such algorithms may imply. Furthermore, state-of-the-art coordination algorithms rely on very strong assumptions, e.g. the exact knowledge of the position of the vehicles in the system. C-AVOID aims at filling these gaps, building a realistic framework that exploits a specific wireless technology, i.e. the new generation of cellular networks (5G). 5G shows undeniable potential for transportation systems thanks to (i) low end-to-end latency; (ii) edge computational capabilities of the Multi-Access Edge Computing (MEC) platform. Prompt and detailed information based on the direct feedback of connected cars allows to create a profile of typical driver maneuvers and decisions and of localization uncertainties, representing the building block of the new safety applications presented in C-AVOID. Processing latency and communication load will be taken into account through a MEC-enable hardware-in-the-loop simulator, and event-triggering communication will be proposed to counterattack possible application requirement violations.
C-AVOID will be carried out by the Experienced Researcher (ER), which has extensive familiarity with networking performance evaluation and system optimization. The ER will collaborate with supervisor Prof. Ellinas, which experience in Telco systems and patent procedures ensure the overall success of this action.
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology
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Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
1678 Nicosia
Cyprus