A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
This addresses the challenge of efficiently tuning control parameters for autonomous driving systems across diverse vehicles and environments, representing an incremental advancement in automation for deployment.
The paper tackles the problem of accelerating large-scale autonomous driving system deployment by automating control parameter tuning without human intervention, resulting in improved control performance and significantly increased tuning efficiency in simulation and road tests across vehicles in the US and China.
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.