Conclusions and future research directions In this paper, a fault-tolerant VECC architecture with mobility awareness for vehicular task offloading was proposed. The proposed bi-level DQN architecture comprises a level-1 DQN agent for high-level scheduling and level-2 DQN agents operating at each RSU. The level-1 DQN agent determines the optimal RSU for task execution by considering real-time workload and network latency, while the level-2 DQN agents jointly decide the node assignment and select the most appropriate recovery pattern among First Result, Recovery Block, and Retry. [...] From a practical standpoint, the proposed model offers a promising solution for next-generation ITS and vehicular edge networks, where delay-sensitive and safety-critical applications, such as cooperative perception, autonomous driving, and real-time traffic coordination, require reliable and adaptive task execution. The ability to dynamically balance workload distribution and fault-tolerance strategies provides a foundation for resilient and efficient vehicular edge computing deployments. Future research will focus on extending the framework toward scalable and delay-tolerant learning architectures through asynchronous federated DQN mechanisms.