A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment

• URL: http://arxiv.org/abs/2011.04250v1
• Date: Mon, 9 Nov 2020 08:54:36 GMT
• Title: A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
• Authors: Yu Wang, Shu Jiang, Weiman Lin, Yu Cao, Longtao Lin, Jiangtao Hu, Jinghao Miao and Qi Luo
• Abstract summary: The framework consists of three machine-learning-based procedures, which jointly automate the control parameter tuning for autonomous driving. The paper shows an improvement in control performance with a significant increase in parameter tuning efficiency, in both simulation and road tests.
• Score: 5.296964852594282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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