The provided research explores optimizing communication and sensing for autonomous vehicles using millimeter wave technology and orthogonal frequency-division multiplexing. Facing dynamic environments and the impact of wireless conditions on mmWave, the authors propose an adaptive system. This system employs reinforcement learning, considering queue state and channel state information. The goal is to achieve reliable communication and accurate velocity estimation of surrounding objects. To improve performance, they introduce adaptive OFDM and a reward function that utilizes the age of updates. Simulation results using A2C and PPO algorithms demonstrate the superiority of their approach over existing designs.