A Novel Nonlinear Smooth Controller for a Brain-Controlled Driving System in Complex Driving Scenarios

TY - JOUR

T1 - A Novel Nonlinear Smooth Controller for a Brain-Controlled Driving System in Complex Driving Scenarios

AU - Yan, Tianyi

AU - Liu, Siyu

AU - Liu, Mengzhen

AU - Zhang, Deyu

AU - Ming, Zhiyuan

AU - Ma, Lingfei

AU - Luo, Jiawei

AU - Liu, Ziyu

AU - Chen, Qiming

AU - Liu, Tiantian

AU - Song, Yifan

AU - Suo, Dingjie

AU - Zhang, Jian

N1 - Publisher Copyright: © 2000-2011 IEEE.

PY - 2025

Y1 - 2025

N2 - With the rapid advancement of technology, brain-controlled driving (BCD) has emerged as a contemporary focal point of research in academia and industry. BCD refers to the application of brain-machine interface (BMI) technology to driving, where control commands from the human brain are decoded by BMI technology and used to assist in the control of vehicles. However, existing BCD systems display inadequate performance in joint lateral and longitudinal control, and BCD systems in complex driving scenarios with other vehicles have not been studied. In this study, a nonlinear smooth controller is proposed, and a BCD system for complex driving scenarios is developed based on it. First, the BCD system is built from three modules, namely, the vehicle module, the BMI module and the controller module. Subsequently, the nonlinear smooth controller is developed based on the BMI controller, the proximal policy optimization (PPO) controller, and the self-adaptive collaborative (SAC) controller. The SAC controller is designed based on a sigmoid function to achieve nonlinear smoothness in the process of allocating control authority between the PPO controller and the BMI controller. The results of online driving experiments demonstrate that the proposed controller is better equipped to handle complex driving scenarios, exhibiting superior performance, heightened safety, and improved user experience compared to the PPO controller and BMI controller. This study holds significant value in advancing the practicality of BCD and providing a foundation for future research on BMI control.

AB - With the rapid advancement of technology, brain-controlled driving (BCD) has emerged as a contemporary focal point of research in academia and industry. BCD refers to the application of brain-machine interface (BMI) technology to driving, where control commands from the human brain are decoded by BMI technology and used to assist in the control of vehicles. However, existing BCD systems display inadequate performance in joint lateral and longitudinal control, and BCD systems in complex driving scenarios with other vehicles have not been studied. In this study, a nonlinear smooth controller is proposed, and a BCD system for complex driving scenarios is developed based on it. First, the BCD system is built from three modules, namely, the vehicle module, the BMI module and the controller module. Subsequently, the nonlinear smooth controller is developed based on the BMI controller, the proximal policy optimization (PPO) controller, and the self-adaptive collaborative (SAC) controller. The SAC controller is designed based on a sigmoid function to achieve nonlinear smoothness in the process of allocating control authority between the PPO controller and the BMI controller. The results of online driving experiments demonstrate that the proposed controller is better equipped to handle complex driving scenarios, exhibiting superior performance, heightened safety, and improved user experience compared to the PPO controller and BMI controller. This study holds significant value in advancing the practicality of BCD and providing a foundation for future research on BMI control.

KW - Brain-controlled driving (BCD)

KW - brain-machine interface (BMI)

KW - nonlinear smooth controller

KW - self-adaptive collaborative (SAC)

KW - steady state visually evoked potential (SSVEP)

UR - https://www.scopus.com/pages/publications/105006536106

U2 - 10.1109/TITS.2025.3569256

DO - 10.1109/TITS.2025.3569256

M3 - Article

AN - SCOPUS:105006536106

SN - 1524-9050

VL - 26

SP - 9432

EP - 9444

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

IS - 7

ER -