Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Controlling a real-world intersection with connected vehicle information provided by CAMs (Cooperative Awareness Messages)

The project MAVEN (see https://www.maven-ts.eu), funded by the European Com- mission, aims at developing a system for infrastructure-assisted platoon organization and green phase negotiation for automated connected vehicles (ACVs). Vehicle-to-Everything (V2X) communication protocols are hereby used for the insertion of vehicles into a traffic simulation of a real-world intersection. Until now, real world traffic could be inserted into a simulation through stationary detectors, for example magnet field sensors, induction loops, cameras, radar etc. The downside of this detection method is that only momentary information can be obtained and e.g. the behavior of the vehicles approaching an intersection can only be approximated. ACVs however continuously broadcast their positions and speeds via CAMs. Detecting vehicles though these messages leads to a more realistic representation of the vehicle’s driving behavior. The current paper describes how CAMs are used to place and move ACVs inside the simulation of a real-world intersection in Braunschweig with the traffic simulation SUMO (Simulation of Urban Mobility). Furthermore, it describes an approach to how these continuously detected vehicles could be further used as control units. Since the positions and speeds of ACVs are synchronized with the real-world behavior, they can be used to adjust the simulated upstream movements and positioning of conventional vehicles (CV) to match reality. Until all vehicles are equipped with V2X technology, this approach could enable more realistic simulated traffic flow behavior.