Platooning of autonomous vehicles has the potential to increase safety and fuel
efficiency on highways. The goal of platooning is to have each vehicle drive at
a specified speed (set by the leader) while maintaining a safe distance from its
neighbors. Many prior works have analyzed various controllers for platooning,
most commonly linear feedback and distributed model predictive controllers. In
this work, we introduce an algorithm for learning a stable, safe, distributed
controller for a heterogeneous platoon. Our algorithm relies on recent
developments in learning neural network stability certificates. We train a
controller for autonomous platooning in simulation and evaluate its performance
on hardware with a platoon of four F1Tenth vehicles. We then perform further
analysis in simulation with a platoon of 100 vehicles. Experimental results
demonstrate the practicality of the algorithm and the learned controller by
comparing the performance of the neural network controller to linear feedback
and distributed model predictive controllers.