Abstract:

To address excessive iterations and long computation time in the performance simulation of automotive side curtain airbags during vehicle development, a machine learning-based prediction approach was proposed to model two key performance indicators: inflation volume and deployment shape. Leveraging a large repository of enterprise simulation data, a domain knowledge-driven image feature extraction method was proposed, and the XGBoost algorithm was selected for inflation volume prediction through comparative evaluation. Meanwhile, a dual-head neural network based on ResUNet was adopted for deployment shape prediction. The results show that 98.2% of the test samples for the inflation volume prediction model have an error of less than 5%, and 93.3% of the samples for the deployment shape prediction model achieve an intersection-over-union (IoU) greater than 0.8, both meeting engineering accuracy requirements. Compared with traditional simulation methods, the proposed AI models reduce prediction time from two days to the order of seconds, significantly improving iteration efficiency. An effective and feasible technical pathway is provided for automotive safety component development, offering substantial practical value for enterprise digital transformation.

Key words: automotive safety systems, automotive safety airbag, machine learning, performance prediction