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- W3186106330 abstract "This paper presents the design of an automatic (human-out-of-the-loop) control parameters tuning 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." @default.
- W3186106330 created "2021-08-02" @default.
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- W3186106330 date "2021-05-25" @default.
- W3186106330 modified "2023-10-18" @default.
- W3186106330 title "A Learning-Based Automatic Parameters Tuning Framework for Autonomous Vehicle Control in Large Scale System Deployment" @default.
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- W3186106330 doi "https://doi.org/10.23919/acc50511.2021.9482827" @default.
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