異種車両プラトーン向けの安定・安全な分散フィードバック制御器の学習
Learning a Stable, Safe, Distributed Feedback Controller for a Heterogeneous Platoon of Autonomous Vehicles
ニューラルネットワークの安定性証明を活用し、異種車両からなる自動運転プラトーン向けの安定かつ安全な分散制御器を学習。実機4台とシミュレーション100台で線形フィードバックや分散MPCと比較評価した。
著者: Michael H. Shaham, Taskin Padir
分類: cs.LG, cs.AI, cs.MA, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
