Collaborative Control of Human–Machine Game in Lateral and Longitudinal Dimensions Considering Dynamic Allocation of Driving Authority
提出一个横向与纵向维度的人机博弈协同控制框架，通过动态调整驾驶权来提升人机共驾的安全、稳定和效率，对自动驾驶和智能座舱设计有参考价值。
In the process of human-machine collaborative driving, it is crucial to ensure that the driver and the machine operate the vehicle in a safe, stable, and efficient manner. However, most of the current studies focus on the lateral shared control under the condition of constant longitudinal speed, without considering the influence of longitudinal speed change on lateral control. Therefore, this article proposes a collaborative control framework of human-machine game in lateral and longitudinal dimensions considering dynamic allocation of driving authority to improve the collaborative performance of co-driving. First, a human-machine collaborative driving system model that adapts to the characteristics of co-driving mode is built as the basis of the shared control scheme. Then, the unconscious competitive relationship of human-machine is described as the game interaction relationship, with optimal control strategies for both sides being deduced theoretically at the game equilibrium. Additionally, a dynamic adjustment strategy of driving authority considering the longitudinal speed is established based on the assessment of lateral and longitudinal risks. Finally, the driver-in-the-loop test and co-simulation results show that the proposed control strategy has achieved good performance in terms of path tracking, driver’s driving burden, and vehicle stability.