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• W3003649926 startingPage "1361" @default.
• W3003649926 abstract "Using brain signals rather than limbs to drive a vehicle may not only help persons with disabilities to acquire driving ability, but also provide a new alternative interface for healthy people to control a vehicle. However, the longitudinal driving performance of brain-controlled vehicles (BCVs) at a relatively high speed is not good enough. In this paper, to improve the performance of the longitudinal brain-control driving, we propose a new predictive control method based on the models of human behaviors and vehicle dynamics. The proposed method is designed to maintain rear-end safety of BCVs and driver ride comfort while ensuring the maximum control authority of brain-control drivers. Driver-and-hardware-in-the-loop experiments are conducted with different subjects under three kinds of scenarios to validate the proposed method. The results show that the proposed method is effective in maintaining rear-end safety and driver ride comfort while preserving driver intention." @default.
• W3003649926 created "2020-02-07" @default.
• W3003649926 creator A5009651513 @default.
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• W3003649926 date "2021-03-01" @default.
• W3003649926 modified "2023-10-14" @default.
• W3003649926 title "Human Behavior Model-Based Predictive Control of Longitudinal Brain-Controlled Driving" @default.
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• W3003649926 doi "https://doi.org/10.1109/tits.2020.2969444" @default.
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