A Cloud–Edge–Vehicle Framework for Task Offloading With Trajectory Prediction Information

With the rapid advancement of autonomous driving technology, the increasing computational demands of intelligent vehicles have driven the adoption of cloud and edge computing to augment limited onboard resources. However, this cloud–edge integration presents new challenges for efficient task offloading. In addition, the high mobility of vehicles further complicates the design of reliable offloading strategies. To address these challenges, this article proposes a cloud–edge–vehicle (CEV) framework that leverages predictable vehicle trajectories for optimized task offloading. A spatio-temporal multihead self-attention long short-term memory (ST-MHSA LSTM) model is designed to accurately predict vehicle trajectories by capturing motion trends and interactions with neighboring vehicles. Building upon the trajectory prediction information, a deep reinforcement learning (DRL)-based task offloading algorithm is proposed. This algorithm incorporates a dynamic priority assignment strategy to prioritize delay-sensitive tasks according to their urgency, thereby improving offloading performance and reducing system costs associated with task transmission and execution. To mitigate the adverse effects of inevitable trajectory prediction errors, a prediction consistency-based deviation correction strategy is further introduced, enhancing decision robustness in dynamic scenarios. Simulation results show that as task numbers increase, the proposed framework outperforms traditional methods (i.e., local, edge, cloud, and random computing) in task success ratio, processing delay, and energy consumption. Task success ratio improves by 115.79%, 114.44%, 27.09%, and 135.19% over local, edge, cloud, and random computing, respectively. Average processing delay is reduced by 70.47%, 62.23%, 38.69%, and 79.85%, while average energy consumption decreases by 65.33%, 66.59%, 59.12%, and 74.66%. These results highlight the framework’s superior performance for computation-intensive and delay-sensitive vehicular applications.