Recent advances in sensing technologies, wireless communications, and computing paradigms drive the evolution of vehicles in becoming an intelligent and electronic consumer products. This paper investigates enabling digital twins in vehicular edge computing (DT-VEC) via cooperative sensing and uploading, and makes the first attempt to achieve the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via vehicle-to-infrastructure (V2I) communications. The digital twins are modeled based on the sensed information, which are utilized to from the logical view to reflect the real-time status of the physical vehicular environment. Second, we derive the cooperative sensing model and the V2I uploading model by considering the timeliness and consistency of digital twins, and the redundancy, sensing cost, and transmission cost. On this basis, a bi-objective problem is formulated to maximize the system quality and minimize the system cost. Third, we propose a solution based on multi-agent multi-objective (MAMO) deep reinforcement learning, where a dueling critic network is proposed to evaluate the agent action based on the value of state and the advantage of action. Finally, we give a comprehensive performance evaluation, demonstrating the superiority of MAMO.

翻译：近年来，传感技术、无线通信与计算范式的进步推动汽车向智能化电子消费产品演进。本文通过协同感知与上传技术，首次探索在车载边缘计算中实现数字孪生（DT-VEC）的质量-成本权衡。首先，提出一种DT-VEC架构：车辆可感知异构信息，并通过车对基础设施（V2I）通信上传至边缘节点。基于感知信息构建的数字孪生用于形成逻辑视图，以反映物理车载环境的实时状态。其次，考虑数字孪生的时效性与一致性，以及冗余、感知成本与传输成本，推导出协同感知模型与V2I上传模型。在此基础上，建立双目标优化问题以最大化系统质量并最小化系统成本。再次，提出基于多智能体多目标深度强化学习（MAMO）的解决方案，其中设计对偶竞争评判网络，通过状态价值与动作优势评估智能体行为。最后，通过综合性能评估验证MAMO的优越性。