Learning a Stable, Safe, Distributed Feedback Controller for a Heterogeneous Platoon of Vehicles
CoRR(2024)
摘要
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 some 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 and safety
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.
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