  Connected and automated vehicles (CAVs) provide the most intriguing
opportunity for enabling users to monitor transportation network conditions and
make better decisions for improving safety and transportation efficiency. In
this paper, we address the problem of effectively coordinating CAVs on
lane-based roadways. Our approach utilizes potential functions to generate
repulsive forces between CAVs that ensure collision avoidance. However, such
potential functions can lead to unrealistic acceleration profiles and large
inter-vehicle distances. The primary contribution of this work is the
introduction of performance-sensitive potential functions to address these
challenges. In our approach, the parameters of a potential function are
determined through an optimization problem aiming to reduce both acceleration
and inter-vehicle distances. To circumvent the computational implications due
to the complexity of the resulting optimization problem that prevents the
derivation of a real-time solution, we train a neural network model to learn
the mapping of initial conditions to optimal parameters derived offline. Then,
we prove sufficient criteria for the sampled-data model to ensure that the
neural network output does not activate any of the state and safety
constraints. Finally, we provide simulation results to demonstrate the
effectiveness of the proposed approach.
