Digital twin (DT) technology offers transformative potential for vehicular networks, enabling high-fidelity virtual representations for enhanced safety and automation. However, seamless DT synchronization in dynamic environments faces challenges such as massive data transmission, precision sensing, and strict computational constraints. This paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for DT-assisted vehicular networks in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to serve vehicles while simultaneously utilizing millimeter-wave (mmWave) radar for environmental mapping. We implement particle filtering at RSUs to achieve high-precision vehicle tracking. To optimize performance, we formulate a joint optimization problem balancing semantic communication rates and sensing accuracy under limited computational resources and power budget. Our solution includes a hybrid heuristic algorithm for vehicle-to-RSU assignment and an alternating optimization approach for determining semantic extraction ratios and beamforming matrices. Performance is extensively evaluated via the Cramér-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and resource utilization. Numerical results demonstrate that the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing integrated sensing and communication (ISAC) schemes under constrained resource conditions.

翻译：数字孪生技术为车联网提供了变革性潜力，通过高保真虚拟表征赋能安全增强与自动化。然而，动态环境中的无缝数字孪生同步面临海量数据传输、精密感知及严格计算约束等挑战。本文针对近场场景下数字孪生辅助车联网，提出一种集成感知、计算与语义通信框架。基于多用户多输入多输出配置，每个路侧单元通过语义通信服务车辆，同时利用毫米波雷达进行环境建图。我们采用路侧单元粒子滤波实现高精度车辆追踪。为优化性能，构建了在有限计算资源与功率预算下平衡语义通信速率与感知精度的联合优化问题。所提方案包含用于车辆-路侧单元分配的混合启发式算法，以及确定语义提取比率与波束赋形矩阵的交替优化方法。通过角度与距离估计的克拉美-罗界、语义传输速率及资源利用率进行系统性能评估。数值结果表明，在资源受限条件下，所提框架在保持现有集成感知与通信方案感知精度的同时，传输速率提升20%。