  Connected and automated vehicle (CAV) technology is one of the promising
solutions to addressing the safety, mobility and sustainability issues of our
current transportation systems. Specifically, the control algorithm plays an
important role in a CAV system, since it executes the commands generated by
former steps, such as communication, perception, and planning. In this study,
we propose a consensus algorithm to control the longitudinal motion of CAVs in
real time. Different from previous studies in this field where control gains of
the consensus algorithm are pre-determined and fixed, we develop algorithms to
build up a lookup table, searching for the ideal control gains with respect to
different initial conditions of CAVs in real time. Numerical simulation shows
that, the proposed lookup table-based consensus algorithm outperforms the
authors' previous work, as well as van Arem's linear feedback-based
longitudinal motion control algorithm in all four different scenarios with
various initial conditions of CAVs, in terms of convergence time and maximum
jerk of the simulation run.
