Shared control based on ANFIS for brain-controlled driving

Abstract

Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver.