At present, autonomous driving technology focuses on how to proactively avoid collisions. However, when it comes to scenarios of inevitable collisions caused by the intrusion of other traffic participants, few studies have explored how to reduce the severity of the accidents by predicting such severity under different vehicle driving modes. To address this issue, a two-layer stacking accident severity prediction model is proposed; it was trained on the NASS-CDS dataset, which is composed of real-world accident data. The model takes accident-related features that can be perceived by vehicle sensors as input and outputs the highest level of injury to passengers. In the first layer, different learner combinations are trained through experiments, and K-Nearest Neighbor(KNN), adaptive boosting, and extreme gradient boosting are eventually selected as base learners considering both predictive performance and algorithm time consumption. In the second layer, logistic regression is selected as a meta learner to reduce overfitting. Experimental results show that the classification accuracy of the proposed model reaches 85.01%, which is superior to other individual and integrated models in terms of precision, recall, and F1 value. The predicted results can be used as a priori information for the decision-planning module of intelligent vehicles to help vehicles make correct decisions and mitigate accident damage. Finally, the application of the model in L2 assisted driving and L4 autonomous driving vehicles is reported in detail. The proposed approach further improves the safety of vehicles based on conventional protection
