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

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.

Automated summary

This paper proposes a new predictive control method to improve the longitudinal driving performance of brain-controlled vehicles, focusing on maintaining rear-end safety and driver ride comfort while ensuring maximum control authority for brain-control drivers. Driver-and-hardware-in-the-loop experiments validate the effectiveness of the proposed method in preserving rear-end safety, driver ride comfort, and driver intention.