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Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance. Two control subsystems, active front-wheel steering (AFS) and active rear-wheel steering (ARS) systems, are emulated as a dynamic non-zero-sum game to explore a more flexible balance between the dual objectives of path-tracking accuracy and vehicle stability. Finally, the control policy iterative equalities of the AFS and ARS systems are constructed utilizing the coupled game Riccati equation and Kronecker product. Adaptive dynamic programming (ADP) has been employed to iteratively update and learn the optimal shared strategy without relying on accurate knowledge of driver steering characteristics and vehicle dynamics. Simulations demonstrate the convergence and adaptability of the proposed strategy in different road scenarios. In addition, our shared control scheme can effectively assist drivers with different characteristics to achieve ideal steering control performance and reduce their driving workload.
Guo et al. (Tue,) studied this question.