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The explosive growth of data with the requirements of high reliability and low latency has posed huge challenges to the vehicular networks. One potential solution is to deploy the fog computing servers geographically closer to the vehicles to serve the vehicle-based applications in real time. However, due to the constraint of caching storage space as well as lack of tractable access mode selection and spectrum allocation algorithm, it is very challenging to balance the network transmission performance and fronthaul savings. In this paper, we investigate the joint optimization problem of access mode selection and spectrum allocation in fog computing-based vehicular networks. To solve the problem in an efficient way, the original high complexity optimization problem is divided into two subproblems. Wherein, the vehicle access mode selection problem is solved by Q-learning-based algorithm by considering spectrum allocation profiles and the fronthaul link cost. The randomly vehicular network topologies are modeled as CoX processes and the closed-form payoff expressions are derived by the stochastic geometry tools. On the other hand, the optimal spectrum allocation indicator value can be finally obtained via convex optimization. The analytical results for the proposed algorithm as well as the traditional baseline approaches are evaluated with different weight factors, which verify our theoretical analysis and confirm the proposed approach can achieve significant performance gains.