When a vehicle collision occurs, the Advanced Automatic Crash Notification (AACN) system can predict the driver’s injury in the vehicle based on the driver’s injury prediction algorithm. It helps the rescue center make early judgments as well as positive and effective decisions, which improves rescue efficiency and saves more lives of seriously injured drivers. First, by selecting the amount of speed change, the direction of the accident, the driver’s age, gender, whether to wear the seat belt, and whether the driver’s side airbag inflated as the influencing factors of the driver’s injury, a Logistic regression model was analyzed and built based on road accident data. Next, the effectiveness of the model was verified by using the Hosmer-Lemeshow test table, and the best trigger threshold was obtained through sensitivity analysis. Then, a driver injury prediction algorithm was proposed, and the overall design of the AACN system terminal was realized based on this algorithm. Finally, an actual case was used to test the accuracy of the injury prediction algorithm. The results of the case study show that the proposed driver injury prediction algorithm has a high accuracy rate, can effectively predict the driver’s injury, and improve the accuracy of the AACN system.