<p>The task processing delay and road safety are the key challenges of the vehicle-to-everything (V2X) network in the sixth generation system. By offloading the computation-intensive tasks of the vehicles to road side unit (RSU) and base station (BS), mobile edge computing (MEC) technology can reduce the processing delay of V2X network. However, it is difficult to dynamically associate the moving vehicles to MEC servers and offload the tasks, especially in the low collision scenario. Thus, we consider the MEC-assisted multi-vehicle V2X system, where the vehicles can offload the computation-intensive tasks to the MEC servers deployed at the multi-antenna RSUs and BS with the zero-forcing receivers. The system delay minimization problem is formulated under the delay and collision constraints to satisfy the task processing delay and safety requirements in the V2X system. Due to the coupling of the association, offloading ratio and driving acceleration, the system delay minimization problem is difficult to solve. Thus, the intelligent scheme based on deep Q-network is proposed to jointly optimize the association, offloading ratio, driving acceleration. The piecewise reward function is designed depending on the delay, energy and collision constraints, while the proposed algorithm trains the vehicles to obtain superior actions consisting of MEC server association, offloading ratio and driving acceleration. Simulation results show that the system delay of the proposed algorithm can reduce by 16.44% and 26.64% compared with Q-learning and local computing schemes, respectively. Under different road length and number of vehicles settings, the proposed algorithm can also outperform the benchmark schemes.</p>