  This paper investigates the collaboration of multiple connected and automated
vehicles (CAVs) in different scenarios. In general, the collaboration of CAVs
can be formulated as a nonlinear and nonconvex model predictive control (MPC)
problem. Most of the existing approaches available for utilization to solve
such an optimization problem suffer from the drawback of considerable
computational burden, which hinders the practical implementation in real time.
This paper proposes the use of sequential convex programming (SCP), which is a
powerful approach to solving the nonlinear and nonconvex MPC problem in real
time. To appropriately deploy the methodology, as a first stage, SCP requires
linearization and discretization when addressing the nonlinear dynamics of the
system model adequately. Based on the linearization and discretization, the
original MPC problem can be transformed into a quadratically constrained
quadratic programming (QCQP) problem. Besides, SCP also involves
convexification to handle the associated nonconvex constraints. Thus, the
nonconvex QCQP can be reduced to a quadratic programming (QP) problem that can
be solved rather quickly. Therefore, the computational efficiency is suitably
improved despite the existence of nonlinear and nonconvex characteristics,
whereby the implementation is realized in real time. Furthermore, simulation
results in three different scenarios of autonomous driving are presented to
validate the effectiveness and efficiency of our proposed approach.
