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

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.