A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2020
- DOI:
- 10.48550/arXiv.2011.04250
- arXiv:
- arXiv:2011.04250
- Bibcode:
- 2020arXiv201104250W
- Keywords:
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- Computer Science - Robotics;
- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- 8 pages, 12 figures