High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique

High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique

The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. Existing MCAs are typically tuned for worst-case scenarios, limiting their efficiency for medium or slow driving motions. This study proposes a comprehen...

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Bibliographic Details
Main Authors: Mohammad Reza Chalak Qazani, Houshyar Asadi, Zoran Najdovski, Shehab Alsanwy, Muhammad Zakarya, Furqan Alam, Hassen M. Ouakad, Chee Peng Lim, Saeid Nahavandi
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2024-01-01
Series:Cognitive Robotics
Subjects:
Motion-planning
Motion cueing algorithm
Simulator platform
Learning methods
Multilayer perceptron
Simulation
Online Access:http://www.sciencedirect.com/science/article/pii/S2667241324000089
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