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- W4383502893 abstract "This paper presents a deep reinforcement learning-aided controller for a 3-DOF autonomous vehicle with combined lateral and longitudinal dynamics. In this scheme, the active disturbance rejection control (ADRC) gives full play to its advantages of being model-free and being able to estimate and compensate for internal uncertainties and external disturbances in real-time, and deep deterministic policy gradient (DDPG) fully considers safety, comfort, economy, and combines driving demand with state, action, reward to achieve real-time adaptive adjustment of control parameters. Thus, the adaptive controller can better deal with uncertainties from modeling, parameters, and driving environment, and self-learning and adaptation ability is obtained simultaneously. Moreover, simulation results illustrate that the adaptive controller performs satisfactorily for different driving operations and environments due to the online tuning and optimization of control parameters." @default.
- W4383502893 created "2023-07-08" @default.
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- W4383502893 date "2023-05-12" @default.
- W4383502893 modified "2023-09-24" @default.
- W4383502893 title "Reinforcement-learning-aided adaptive control for autonomous driving with combined lateral and longitudinal dynamics" @default.
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- W4383502893 doi "https://doi.org/10.1109/ddcls58216.2023.10166569" @default.
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