  Considering the infrastructure deployment cost and energy consumption, it is
unrealistic to provide seamless coverage of the vehicular network. The presence
of uncovered areas tends to hinder the prevalence of the in-vehicle services
with large data volume. To this end, we propose a predictive cooperative
multi-relay transmission strategy (PreCMTS) for the intermittently connected
vehicular networks, fulfilling the 6G vision of semantic and green
communications. Specifically, we introduce a task-driven knowledge graph
(KG)-assisted semantic communication system, and model the KG into a weighted
directed graph from the viewpoint of transmission. Meanwhile, we identify three
predictable parameters about the individual vehicles to perform the following
anticipatory analysis. Firstly, to facilitate semantic extraction, we derive
the closed-form expression of the achievable throughput within the delay
requirement. Then, for the extracted semantic representation, we formulate the
mutually coupled problems of semantic unit assignment and predictive relay
selection as a combinatorial optimization problem, to jointly optimize the
energy efficiency and semantic transmission reliability. To find a favorable
solution within limited time, we proposed a low-complexity algorithm based on
Markov approximation. The promising performance gains of the PreCMTS are
demonstrated by the simulations with realistic vehicle traces generated by the
SUMO traffic simulator.
