  Vehicle automation technology has made significant progress, laying the
groundwork for a future of fully automated vehicles. This paper delves into the
operation of connected and automated vehicles (CAVs). In prior work, we
developed a controller that includes a tunable gain whose value significantly
influences CAV performance and, in particular, its acceleration. By varying
this gain, CAV acceleration is associated with different values depending on
some initial conditions. Thus, our goal in this paper is to identify the
optimal value of this gain in terms of acceleration for any group of initial
conditions. To this end, we formulate an optimization problem where the
decision variable is the gain value, and the objective function includes the
acceleration of the vehicles. The complexity of this problem prohibits
real-time solutions. To address this challenge, we train a neural network to
map different initial conditions to the optimal gain values efficiently. We
showcase the proposed approach to deriving the optimal gains in a merging
scenario with an on-ramp.
