大規模自動運転展開に向けた学習ベースのチューニング不要制御フレームワーク
A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
車両ごとの制御パラメータ調整を人手なしで自動化するため、学習ベースの動力学モデリング・開ループマッピング・ベイズ最適化による閉ループ調整を組み合わせたフレームワークを提案し、シミュレーションと実車で効率と性能の向上を示した。
著者: Yu Wang, Shu Jiang, Weiman Lin, Yu Cao, Longtao Lin, Jiangtao Hu, Jinghao Miao, Qi Luo
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
This paper presents the design of a tune-free (human-out-of-the-loop parameter tuning) control framework, aiming at accelerating large scale autonomous driving system deployed on various vehicles and driving environments. The framework consists of three machine-learning-based procedures, which jointly automate the control parameter tuning for autonomous driving, including: a learning-based dynamic modeling procedure, to enable the control-in-the-loop simulation with highly accurate vehicle dynamics for parameter tuning; a learning-based open-loop mapping procedure, to solve the feedforward control parameters tuning; and more significantly, a Bayesian-optimization-based closed-loop parameter tuning procedure, to automatically tune feedback control (PID, LQR, MRAC, MPC, etc.) parameters in simulation environment. The paper shows an improvement in control performance with a significant increase in parameter tuning efficiency, in both simulation and road tests. This framework has been validated on different vehicles in US and China.
