To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by transmitting semantic features. We formulate a comprehensive joint optimization problem by optimizing offloading ratios, the number of semantic symbols, and RIS phase shifts. Considering the problem's high dimensionality and non-convexity, we propose a two-tier hybrid scheme that employs Proximal Policy Optimization (PPO) for discrete decision-making and Linear Programming (LP) for offloading optimization. {The simulation results have validated the proposed framework's superiority over existing methods. Specifically, the proposed PPO-based hybrid optimization scheme reduces the average end-to-end latency by approximately 40% to 50% compared to Genetic Algorithm (GA) and Quantum-behaved Particle Swarm Optimization (QPSO). Moreover, the system demonstrates strong scalability by maintaining low latency even in congested scenarios with up to 30 vehicles.

翻译：为在动态环境和间歇性链路中支持时延敏感的车联网应用，本文提出了一种可重构智能表面辅助的语义感知车载边缘计算框架。该方法集成RIS以优化无线连接，并利用语义通信通过传输语义特征来最小化时延。我们通过优化卸载比例、语义符号数量以及RIS相移，构建了一个全面的联合优化问题。考虑到问题的高维性和非凸性，我们提出了一种双层混合方案，该方案采用近端策略优化进行离散决策，并利用线性规划进行卸载优化。仿真结果验证了所提框架相较于现有方法的优越性。具体而言，与遗传算法和量子行为粒子群优化相比，所提出的基于PPO的混合优化方案将平均端到端时延降低了约40%至50%。此外，即使在车辆数量高达30的拥塞场景下，该系统仍能保持低时延，表现出强大的可扩展性。