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- W4381569649 abstract "Autonomous vehicles in public transport can perform a wide range of real-world tasks such as: moving in factories, in public transport, in search and rescue, etc., thus requiring varying degrees of auto-navigation in response to changes in the environment. This paper presents the predictive model (MPC) for the active suspension system, the vehicle's damping system, combined with Deep Q-network (DQN) algorithm reinforcement learning method applied to control self-driving cars in public transport. Intelligent traffic control system to control movement to avoid fixed obstacles or movable obstacles in the event of external interference, taking into account the comfort of the occupants of the vehicle; taking into account the load, the conveying systems; for the purpose of application in the field of intelligent transportation. The research results, built on Matlab Simulink software, show that autonomous vehicles can safely complete intelligent navigation tasks in an unknown environment and become a real system intelligent with the ability to self-study and adapt well to many different environments and various nonlinear factors." @default.
- W4381569649 created "2023-06-22" @default.
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- W4381569649 date "2023-03-25" @default.
- W4381569649 modified "2023-10-16" @default.
- W4381569649 title "Research on Predictive Control for the Damping System of Autonomous Vehicles in the Public Transport on the Basis of Artificial Intelligence" @default.
- W4381569649 doi "https://doi.org/10.14445/23488549/ijece-v10i3p101" @default.
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