Model Predictive-Based Shared Control for Brain-Controlled Driving

Using brain signals rather than limbs to drive a vehicle can help persons with disabilities to extend their movement range and, thus, to improve their self-independence. However, the driving performance of brain-controlled vehicles (BCVs) is poor. In this paper, to improve the performance of BCVs, we propose a new shared control method based on the model predictive control (MPC) strategy. Particularly, to maintain the maximum control authority of brain-control drivers while ensuring the safety of BCVs, the MPC controller is designed by introducing a penalty on the deviation from drivers output in the cost function and setting safety constraints. Driver-and-hardware-in-the-loop experiments are conducted under two road-keeping scenarios and one obstacle-avoidance scenario with different subjects to validate the proposed method. The results demonstrate the effectiveness of the proposed method in avoiding roadway departures and obstacles while maintaining the control authority of users.