  This paper introduces a new approach of treating platoon systems using
mean-variance control formulation. The underlying system is a controlled
switching diffusion in which the random switching process is a continuous-time
Markov chain. This switching process is used to represent random environment
and other random factors that cannot be given by stochastic differential
equations driven by a Brownian motion. The state space of the Markov chain is
large in our setup, which renders practically infeasible a straightforward
implementation of the mean-variance control strategy obtained in the
literature. By partitioning the states of the Markov chain into sub-groups (or
clusters) and then aggregating the states of each cluster as a super state, we
are able to obtain a limit system of much reduced complexity. The justification
of the limit system is rigorously supported by establishing certain weak
convergence results.
