Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

翻译：任务卸载对于高效协调车载无线网络至关重要，这需要获取当前网络状态和计算资源的相关信息。然而，由于车辆的移动性以及近实时执行任务卸载的计算资源有限，此类方案可能产生较高延迟，甚至变得不可行。为解决这一问题，本文提出了一种基于轨迹预测的预卸载决策算法，通过分析车辆的历史轨迹来预测其未来坐标，从而实现计算资源的预先分配。我们首先利用长短期记忆网络模型预测每辆车的运动轨迹。随后，基于任务需求和预测轨迹，我们设计了一种采用双深度Q网络的动态资源分配算法，使边缘服务器能够在确保有效利用可用计算资源的同时，最小化任务处理延迟。仿真结果验证了所提方法的有效性，表明相较于传统的实时任务卸载策略，所提出的TPPD算法在提升资源利用率的同时，显著降低了任务处理延迟。