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Connected and automated vehicles (CAVs) provide the most intriguingopportunity for enabling users to monitor transportation network conditions andmake better decisions for improving safety and transportation efficiency. Inthis paper, we address the problem of effectively coordinating CAVs onlane-based roadways. Our approach utilizes potential functions to generaterepulsive forces between CAVs that ensure collision avoidance. However, suchpotential functions can lead to unrealistic acceleration profiles and largeinter-vehicle distances. The primary contribution of this work is theintroduction of performance-sensitive potential functions to address thesechallenges. In our approach, the parameters of a potential function aredetermined through an optimization problem aiming to reduce both accelerationand inter-vehicle distances. To circumvent the computational implications dueto the complexity of the resulting optimization problem that prevents thederivation of a real-time solution, we train a neural network model to learnthe mapping of initial conditions to optimal parameters derived offline. Then,we prove sufficient criteria for the sampled-data model to ensure that theneural network output does not activate any of the state and safetyconstraints. Finally, we provide simulation results to demonstrate theeffectiveness of the proposed approach.

Performance-Sensitive Potential Functions for Efficient Flow of Connected and Automated Vehicles