<p>The conflict between human drivers and machines in shared control vehicles poses a daunting challenge and research focus. A hierarchical human-machine adaptive cooperative control strategy based on the dynamic game theory is proposed to tackle this issue. This strategy integrates an advanced intention recognition model, an intelligent trajectory planning method, and adaptive dynamic interaction mechanisms to enhance vehicle safety and mitigate human-machine conflicts. The strategy is composed of three layers: intention recognition layer (IRL), trajectory planning layer (TPL), and tracking control layer (TCL). IRL trains a CNN-GRU deep learning model utilizing the data obtained from the driver-in-the-loop platform for intention recognition. This model leverages the feature extraction capabilities of convolutional neural networks (CNN) and the temporal data processing advantages of gated recurrent units (GRU) to accurately identify the driver’s decision intention. Based on the recognized intention, TPL incorporates an improved risk potential field and employs a linear time-varying model predictive control (LTV-MPC) algorithm to plan a dynamic trajectory that aligns with the driver intention while ensuring safety. TCL designs an adaptive weight distribution mechanism under a dynamic game framework to achieve dynamic interaction of human-machine. The time-varying characteristics of the driving scenario and driver operations are considered in TCL. Based on the convex iterative method, the Nash equilibrium solution is obtained to realize the cooperative control. Hardware-in-the-loop experiments are utilized to validate the proposed strategy’s advantage. The results demonstrate that in complex scenarios, cross-layer dynamic synergy can effectively mitigate human-machine conflicts and enhance driving safety.</p>