Vehicular fog computing (VFC) has emerged as a promising research paradigm to address the demands of vehicular application requests. The dynamic nature of VFC poses challenges in distributing fog computing resources to the vehicular tasks. In VFC task scheduling problems, the optimization of the success rate of tasks and minimization of the energy consumption of vehicles ensure efficient completion of critical operations. Moreover, failing to consider the dependencies among the tasks leads to increased delays. Therefore, in this paper, we propose a dependency and mobility-aware intelligent task scheduling (DMITS) mechanism to optimize the success rate of vehicular tasks and energy consumption of vehicles by considering the moving paths of the vehicles, the social relationships among the vehicles and dependencies of tasks. In this study, we model the social relationships among the vehicles based on their communication patterns and social characteristics to improve the success rate of the tasks. We consider the mobility of vehicles using the Markov renewal process (MRP) technique. Initially, we formulate a mixed integer nonlinear programming (MINLP) problem for the proposed problem. Further, we employ a federated deep reinforcement learning mechanism that uses the improved soft actor-critic (iSAC) technique to allocate tasks. We compare our proposed algorithm with existing task scheduling approaches. The simulation results demonstrate that the proposed algorithm performs efficiently in addressing task scheduling problems, achieving higher task success rates and lower energy consumption for vehicles.