Analysis of the Game-Based Human-Machine Co-steering Control on Low-Adhesion Road Surfaces

With the progressing autonomy of driving technology, machine is assuming greater responsibility for driving tasks to enhance safety. Leveraging this potential, this paper introduces a novel human-machine co-steering control strategy based on model predictive control. The strategy is designed to address the difficulties faced by drivers when driving on surfaces with low adhesion. Firstly, the proposed strategy utilizes a parallel human-machine co-steering framework with a weight allocation concept between the controller and the driver. Moreover, the nonlinear controller dynamics model and linear driver dynamics model are developed to characterize the interaction behaviors between human and machine under low-adhesion road surface conditions. And a nonlinear game optimization problem is formulated to capture the cooperative interaction relationship between human and machine. Finally, to solve the nonlinear game optimization problem, piecewise affine linearization method is employed to derive the analytical solution of the human-machine co-steering optimal strategy. This approach enables the determination of the optimal co-steering control strategy for both human and machine components under low-adhesion road surface conditions. Experimental results demonstrate that assigning a higher driving weight to either the controller or the driver leads to increased human-machine driving conflicts. Conversely, assigning equal driving weights to both the controller and the driver minimizes the human-machine driving conflict, where the driver has virtually no influence on the driving of the intelligent vehicle. Furthermore, the human-machine driving conflict is smaller in scenarios where the driving weights are equal. The results show that the proposed method provides an effective assistance for drivers on low-adhesion surfaces.