  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.
