CPA-MAC: A Collision Prediction and Avoidance MAC for Safety Message Dissemination in MEC-Assisted VANETs

Abstract

Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, resulting in high transmission delay and packet collision. To overcome these shortcomings, we propose a collision prediction and avoidance MAC (CPA-MAC) protocol that utilizes the capability of mobile edge computing (MEC) and machine learning in this paper. Specifically, we propose a new slot assignment method that aims to guarantee the high channel utilization and low delay of safety message under dynamic traffic conditions. Furthermore, we propose a new same-direction collisions prediction algorithm that combines the V2R communication and LSTM-based trajectory prediction algorithm. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.