Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to solve offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.

翻译：语义通信与车联网边缘计算相结合，为车联网提供了一种高效的边缘任务处理范式。本文聚焦于高速公路场景，提出了一种三方协同语义通信框架，使车辆用户能够通过车-路通信与车-车通信实现语义任务卸载。该框架综合考虑任务时延与语义符号数量，构建了一个混合整数非线性规划问题，并将其转化为两个子问题。首先，我们创新性地提出了一种基于参数化分布噪声的多智能体近端策略优化任务卸载方法，以求解语义符号数量的优化问题；其次，采用线性规划方法求解卸载比例。仿真结果表明，该方案的性能优于其他对比算法。