Adaptive Data Transmission and Computing for Vehicles in the Internet-of-Intelligence

Efficient scheduling of vehicle resources is of great significance to guarantee vehicle safety and to achieve a higher level of automated driving. Considering the performance fluctuations in data transmission and processing during driving, this paper proposes an adaptive data transmission and computation optimization scheme, where the concept of the Internet-of-Intelligence is introduced to improve the resource decision-making efficiency through knowledge sharing instead of data sharing among vehicles. Specifically, the joint optimization problem is formulated to minimize the long-term energy consumption with the consideration of the average queuing latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem into a series of short-term deterministic optimization subproblems. Afterwards, both the optimization-based solution and the learning-based solution are presented to fully illustrate the performance advantages of Internet-of-Intelligence applied to vehicle networks. The former can output the global optimal solution by iteration, while the latter aims at accelerating the distributed optimization decision-making through building a fast deep reinforcement learning framework based on shared knowledge among vehicles. Simulation results show the advantages of the proposed scheme in stability, energy consumption, and latency, and it also verifies the convergence speed and training accuracy of the proposed fast deep reinforcement learning framework.