We propose deploying an integrated multi-technology solution built on i) C-V2X–based infrastructure, ii) DSRC based infrastructure to broadly enable both direct and network-assisted communication between vehicles, and roadside
infrastructure including traffic lights, 5G base stations etc., thereby supporting a reliable data and communication network towards autonomous driving. The easy to scale infrastructure will include Roadside Units (RSUs) and the required
transmission protocols and messages, such as Signal Phase and Timing (SPaT). While the primary infrastructure will rely on C-V2X RSUs, many commercially available models, such as the Cohda MK6 RSU, Commsignia ITS-RS4, and ETTIFOS SIRIUS, also support DSRC and C-V2X LTE or 5G network connectivity. This multi-technology capability provides flexibility, allowing the deployment to be tailored to Tartu city and other cities preferences, available resources, and long-term strategy. Note that the selection of RSUs will adhere to EU security guidelines, ensuring procurement from vendors that meet compliance
requirements. The RSUs will be integrated with the traffic light controllers currently deployed in Tartu, e.g., Swarco ITC-3.
The solution’s key components include:
Reliable and Low-Latency Communication:
Use standardized PC5 interface for direct V2V/V2I communication and Uu interface for V2N connectivity, enabling safetycritical operations and higher automation levels (Level 4–5).
Machine-Readable Traffic Lights:
Connect RSUs to either NTCIP-compliant traffic light controllers (e.g., Swarco ITC-3) or through middleware, if needed, to transmit SPaT, i.e., traffic light status information, data for autonomous vehicles.
Aggregated Mobility Data Collection:
Our solution will collect additional data to remove the existing fragmentation and use the existing data. Aggregate the mobility pattern data from the connected vehicles via V2N to a central server. Enhance weak data using
sensor fusion and AI-based intersection analysis (e.g., YOLOv11 instance segmentation). The mobility pattern analysis will incorporate data from C-V2X–connected vehicles, high-precision positioning information (building on TalTech teams extensive experience in vehicle positioning from European projects such as 5G-ROUTES, LATEST5S, 5G-BALTICS), and stateof-
the-art intersection-level mobility pattern data obtained through AI-based instance-segmentation frameworks. V2N enables aggregated data collection and analysis from the connected vehicles, which allows city-level data-driven planning for traffic optimization, climate impact modeling, and decision-making.
