Human Driving Skill Modeling Using Neural Networks for Haptic Assistance in Realistic Virtual Environments
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This work addresses our research on driving skill modeling using artificial neural networks for haptic assistance. In this paper, we present a haptic driving training simulator with performance-based, error-corrective haptic feedback. One key component of our simulator is the ability to learn an optimized driving skill model from the driving data of expert drivers. To this end, we obtain a model utilizing artificial neural networks to extract a desired movement of a steering wheel and an accelerator pedal based on the experts' prediction. Then, we can deliver haptic assistance based on a driver's performance error which is a difference between a current and the desired movement. We validate the performance of our framework in two respective user experiments recruiting expert/novice drivers to show the feasibility and applicability of facilitating neural networks for performance-based haptic driving skill transfer.
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