Human Behavior Model-Based Predictive Control of Longitudinal Brain-Controlled Driving

Yun Lu*, Luzheng Bi

*Corresponding author for this work

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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.

Original languageEnglish
Article number8975986
Pages (from-to)1361-1374
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number3
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Brain-controlled vehicles (BCVs)
  • human behavior modeling
  • longitudinal control
  • predictive control

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Lu, Y., & Bi, L. (2021). Human Behavior Model-Based Predictive Control of Longitudinal Brain-Controlled Driving. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1361-1374. Article 8975986. https://doi.org/10.1109/TITS.2020.2969444