Leveraging RL for Efficient Collection of Perception Messages in Vehicular Networks

Cooperative messages play a vital role in vehicle-to-everything (V2X) applications by enhancing situational awareness, supporting collision avoidance and improving traffic efficiency. Additionally, they contribute to Vulnerable Road Users (VRU) safety by increasing environment perception. The purpose of this paper is to introduce a novel Q-Learning technique that can improve the selection of cooperative messages' type, size, and frequency. The methodology is based on leveraging the diversity of existing messages in vehicular networks to determine the best message type with the appropriate size while adjusting its transmission frequency according to the environmental context to efficiently manage network resources. In addition to alleviating the network overload and decreasing the number of messages sent simultaneously, our method could result in significant energy savings when applied to VRUs when they are identified by Connected and or Autonomous Vehicles (CAV).