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Rider model identification using dynamic neural networks

Abstract : Car driver modeling is a well-known research topic, with significant existing contributions. In contrast, important questions related to motorcyclist modeling remain unanswered. This study focuses on identifying a motorcyclist model that can predict the steering angle and the rider roll angle. A black box rider model in the form of a time delay neural network is presented. This model was developed using experimental data recorded with an instrumented motorcycle from the VIROLO++ research project. It is used for three main issues. First, the selection of input signals and their impact on prediction performance is discussed. Next, the model’s ability to predict the behavior of a variety of motorcyclists is demonstrated. Finally, the nonlinearity of the model is analyzed. These results pave the way to the development of a cybernetic rider model.
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Contributor : Fabien Claveau Connect in order to contact the contributor
Submitted on : Friday, April 29, 2022 - 3:01:56 PM
Last modification on : Friday, August 5, 2022 - 2:54:51 PM
Long-term archiving on: : Saturday, July 30, 2022 - 6:49:10 PM


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Paul Loiseau, Chaouki Nacer Eddine Boultifat, Philippe Chevrel, Fabien Claveau, Stéphane Espie, et al.. Rider model identification using dynamic neural networks. IFAC World Congress 2020, Jul 2020, BERLIN, Germany. pp.15346-15352, ⟨10.1016/j.ifacol.2020.12.2347⟩. ⟨hal-02498139⟩



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