Enhanced Driver Awareness through Cooperative XR Technologies
With the recent technological breakthroughs in connected and semi-automated transport, there is no doubt that a shared vision for a large-scale deployment of Cooperative Intelligent Transport Systems (C-ITS) in the European Union exists4. Connected and Automated Vehicles (CAVs) are expected to be the cornerstone of tomorrow’s EU-envisioned envisioned Cooperative, Connected, and Automated Mobility (CCAM), being able to extend their perception capabilities beyond the range of individual sensors, boosting the drivers’ situational awareness and enhancing performance, safety and comfort.
Equally importantly, 5/6G-enabled vehicular networks are expected to minimize network latency and delay, provide critical information regarding the environment, in a timely manner, and meet human reaction times in visual feedback.
In this framework, SafeDriveXR will develop a new 5/6G-enabled paradigm incorporating multiple heterogeneous devices that cooperate to provide drivers with highly interactive eXtended Reality (XR) interfaces and tools for real-time visualizations of traffic, road conditions, and obstacles, helping them to improve decision-making, reduce response times to hazards, and increase overall road safety.
This will be achieved by novel distributed and cooperative signal processing and learning approaches integrated over the cloud edge continuum, capitalizing dynamic 5/6G-enabled cloud-to-edge services across the vehicular network.
These will enable the accurate identification of user situational awareness of different critical situations and the correct and timely detection of road obstacles, vehicles, and Vulnerable Road Users (VRUs).
The novel strategies for service component distribution and intelligent coordination will significantly reduce communication latencies, meeting human reaction time in visual XR feedback.
The use of XR interfaces and QoE evaluation methodologies will boost personalization & adaptation of the rendered information based on user context.
