In Vehicular Edge Computing （VEC） environments， the high mobility and uneven distribution of vehicles often lead to cross-domain task offloading failures and load imbalance of RoadSide Units （RSUs）， thereby degrading system performance significantly. The existing studies mainly focus on load balance or single-step offloading decisions， while failing to address the communication uncertainties caused by vehicle mobility and the requirement for flexible task scheduling across RSUs， which limits offloading efficiency. Therefore， a Trajectory-Aware Collaborative Multi-Agent Reinforcement Learning （TAC-MARL） method was proposed. First， foresighted mobility awareness for task scheduling was realized through introducing a Patch Time Series Transformer （PatchTST） -based multi-step vehicle trajectory prediction module to guide task scheduling. Second， a vehicle-edge-cloud collaborative task offloading framework was constructed， in which vehicles and RSUs were modeled as agent groups， cooperative decision-making was performed by the employment of a reinforcement learning paradigm — Centralized Training and Decentralized Execution （CTDE）， and a partially reward-decoupled multi-agent policy optimization method was designed to improve cooperation efficiency and system stability. Simulations under varying task densities， latency constraints， and network topologies were conducted. The proposed method consistently outperforms baseline algorithms such as Independent Proximal Policy Optimization （IPPO）， Multi-Agent Deep Deterministic Policy Gradient （MADDPG）， and Multi-Agent Proximal Policy Optimization （MAPPO） in task completion rate and task completion latency， verifying the efficiency， robustness， and practical potential of the proposed method in dynamic and complex VEC environments.
