Analysis of the Game-Based Human-Machine Co-steering Control on
Low-Adhesion Road Surfaces 2023-01-7086
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.