  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.
