With the rapid development of autonomous driving technology, the issue of vehicle control takeover has become a prominent research topic. A car equipped with an assisted driving system cannot fully handle all driving scenarios. When the actual driving scenario exceeds the operational design domain of the assisted system, human intervention is still required to control the vehicle and ensure the safe completion of the driving task. Takeover performance is an extremely important metric for evaluating a driver’s performance during the takeover process, which includes takeover reaction time and takeover quality. The takeover reaction time refers to the time from the system’s takeover request to the driver’s control of the steering wheel. The length of the takeover response time not only reflects the driver’s current state but also affects the subsequent handling of complex scenarios. Takeover quality refers to the quality of manual vehicle operation by the driver after regaining control. This study, based on the CARLA driving simulator, constructs 6 typical driving scenarios, simulates the vehicle control takeover process, and collects physiological signals and eye movement data from 31 drivers using a multi-channel acquisition system. Based on the driver’s takeover performance, and regarding International standards, an objective takeover performance evaluation metric is proposed, incorporating the driver’s takeover reaction time, maximum horizontal and vertical accelerations, and minimum collision time, derived from multiple vehicle data. By combining driver data, vehicle data, and scenario data, a deep neural network (DNN) model predicts takeover performance, while the SHAP model analyzes the impact of each feature, improving the model’s interpretability and transparency. The experimental results show that the proposed DNN model outperforms traditional machine learning methods in predicting takeover performance, achieving an accuracy of 92.2% and demonstrating good generalization. The SHAP analysis reveals the impact of key features such as heart rate variability, driving experience, and minimum safe distance on the prediction results. This research provides a theoretical and empirical foundation for the safety optimization and human-computer interaction design of autonomous driving systems and is of great significance for improving the efficiency and safety of human-vehicle cooperation in autonomous driving technology.