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Contrôle plateforme pour la validation du véhicule autonome sur simulateur dynamique à hautes performances

Abstract : The work presented in this manuscript takes part in the context of driving simulation and more specifically in the one of dynamic driving simulators used for the validation of advanced systems and the autonomous vehicle. In order to address the issues of performance and motion perception, we have presented different approaches to improve the Motion Cueing Algorithms (MCA). All our studies show that the model predictive control (MPC) strategy is the best choice to MCA on high-performance driving simulators. Indeed, compared to other strategies, it allows to better take advantage of the workspace without endangering the simulator and/or the driver. However, in this MCA, the real-time optimization and the perception model must be guaranteed in order to improve the driver's immersion in the virtual environment. Therefore, we compared different techniques to solve constrained optimization problems. We proposed a based optimization technique, which provides an intuitive and fast solution to the MPC constrained optimization problem. Finally, we established recommendations for MCA parameterization according to the self-declared driving behavior allowing a better perception of motion in a driving simulator, in interactive driving and in autonomous mode.
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Submitted on : Wednesday, December 9, 2020 - 1:02:10 PM
Last modification on : Friday, August 5, 2022 - 2:54:01 PM
Long-term archiving on: : Wednesday, March 10, 2021 - 7:08:29 PM


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  • HAL Id : tel-03048468, version 1


Carolina Rengifo Cadavid. Contrôle plateforme pour la validation du véhicule autonome sur simulateur dynamique à hautes performances. Automatique / Robotique. HESAM Université, 2020. Français. ⟨NNT : 2020HESAE023⟩. ⟨tel-03048468⟩



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