Vehicular Adhoc Networks (VANETs) enable vehicles to communicate with each other and with the infrastructure to improve safety, efficiency, and mobility. Event-based communication models have emerged as a promising approach for VANETs to detect and respond to various traffic events in real-time. Machine learning algorithms have been used to detect and classify events, optimize communication parameters, and predict accidents. This work cover a wide range of applications, including safety, traffic analysis, cooperative perception, routing, and communication. The machine learning algorithms used include deep learning, clustering, support vector machines, random forests, recurrent neural networks, and reinforcement learning. The performance evaluation demonstrate the potential of event-based communication models for enhancing the performance and security of VANETs. However, challenges and limitations still exist, such as data privacy, network scalability, and robustness against adversarial attacks. Future research can explore new machine learning algorithms, data fusion techniques, and communication protocols to address these challenges and advance event-based communication models in VANETs.

machine learning, vehicular ad hoc networks, VANETs, traffic congestion, broadcast storm, content delivery, event detection, parameter analysis, performance evaluation, intelligent transportation systems.