The rapid development of intelligent driving technology has made human-machine collaborative co-driving a key paradigm for balancing automation capabilities with human driving rights and responsibilities. Driving authority takeover technology, which ensures the safe, smooth, and efficient transfer of control between humans and machines, has become one of the core challenges and technical bottlenecks in this field. By systematically reviewing relevant research on theoretical frameworks, technical challenges, and interaction mechanisms, the limitations of driving authority takeover technology in real-time decision-making and personalized adaptation are highlighted. This enables a thorough analysis of current progress and clarifies future research directions. First, from a multidisciplinary perspective, this study elaborates on the theoretical foundation of driving authority takeover, proposes a classification framework based on scenario characteristics, and analyzes the roles of factors such as environmental complexity and driver state. In addition, the wake-up strategies and control algorithms are systematically compared, highlighting the shortcomings of current technology in adapting to complex scenarios and personalized designs. Second, the influence of human-machine trust on driving authority takeover is innovatively explored. From the two dimensions of trust dynamic modeling and multimodal interaction, a trust calibration-based optimization method for takeover strategies is proposed. Finally, the trend toward integrating large models with cross-modal cognitive technology is envisioned, providing research directions for seamless human-machine collaborative driving in the future.