The motion cueing algorithm (MCA) enables life-like motion in simulators resembling real driving. Regenerated motions must adhere to workspace constraints. Vehicle motion signals (linear acceleration, angular velocity) are generated in a simulated vehicle environment utilised in MCA for motion cues. These signals are categorised into levels (slow, medium, fast) based on frequency and amplitude. The commonly used MCA, the classical washout filter, is typically fine-tuned using worst-case (fast-driving) scenarios to meet the simulator’s requirements across various situations. However, this approach reduces the MCA’s effectiveness in handling slower driving scenarios, resulting in conservatism in platform workspace usage for slow and medium driving. Consequently, a noticeable motion sensation error arises between real vehicle drivers and motion simulator users. To rectify this issue, a novel neural network-based MCA is developed in this study. Three distinct classical washout filters are meticulously tuned to cater to slow, medium, and fast driving scenarios. These filters generate precise motion cues for simulator users at corresponding levels of driving scenarios. The neural network-based MCA is constructed using the synthesised signals from these classical washout filters. This proposed method is thoroughly validated through the utilisation of MATLAB software. In direct comparison with the standard classical washout filter, the proposed MCA significantly reduces the motion sensation error, enriches motion fidelity, and optimises the utilisation of the simulator’s workspace.
A Neural Network-Based Motion Cueing Algorithm Using the Classical Washout Filter for Comprehensive Driving Scenarios
IEEE Transactions on Intelligent Transportation Systems ; 25 , 6 ; 5112-5121
2024-06-01
8481438 byte
Article (Journal)
Electronic Resource
English
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