Path Selection Based on Network Service Quality for Infrastructure-Assisted Automated Driving

Infrastructure-assisted automated driving will highly rely on real-time updates received from the network about the surroundings, which demand for ultra-low latency, high-throughput and highly reliable wireless connectivity. In the future, the quality of connectivity will directly impact the driving capability of these autonomous vehicles: lower downlink quality will in fact reduce the situational awareness, forcing the vehicle to, for example, reduce speed and increase safety distance. In this context, we propose a solution which allows a vehicle to choose the route that ensures the best connectivity conditions to reach its destination. Specifically, we consider that different connected and automated driving (CAD) Modes are possible depending on the estimated downlink radio quality, and the route is selected accordingly. Experiments have been conducted through simulations in an urban environment, utilizing the SUMO mobility simulator and the Sionna RT channel modelling tool. The obtained results demonstrate that the network quality can indeed significantly influence route selection, paving the way for future research towards sixth generation (6G) where services and mobility predictions can drive network optimizations.