Trajectory prediction to avoid channel congestion in V2I communications

In the new envisaged paradigm of vehicular communications, critical safety applications have strict requirements in terms of latency and robustness due to the critical nature of their mission that require periodic status exchange and asynchronous event notifications for a reliable cooperative awareness. Current technologies like IEEE 802.11p and recent solutions like fixed period beaconing or moderately reactive adaptive approaches do not cope with these requirements and lead to scalability problems in high density scenarios. With the aim of avoiding channel congestion in those scenarios we make use of trajectory prediction to reduce the number of transmissions required for a full awareness of the vehicle's position and speed. To do so, only a data prediction model of the vehicle's trajectory is sent over the channel. This allows to meet the latency requirements of safety applications while not relying exclusively on MAC congestion control protocols nor modifying the current standard MAC layer. In order to evaluate the proposed approach, we simulate a V2I network in a road intersection under high density traffic taking into account the packet delivery ratio and the predicted position error metrics. Finally, the simulation results show a better position awareness with a reduced channel load compared to the fixed periodic beaconing approach under high density traffic.