Reinforcement Learning Based Transmission Optimization for V2V Unicast Communication

In this paper, we introduce QCBR, a protocol for VANETs in intelligent transportation systems. It addresses challenges, like large Q-learning state space and unstable links, to achieve lower latency and higher message delivery rates while maintaining stability. QCBR includes vehicle clustering, Qvalue table creation, and relay node selection based on stability. Simulations show improved data delivery rates and reduced endto-end delays compared to existing protocols.