Leveraging LSTM Networks for Vehicle Stability Prediction: A Comparative Analysis with Traditional Models Under Dynamic Load Conditions

Abstract

Vehicle stability, especially under dynamic vertical load conditions, is a key factor in vehicle safety and performance. Traditional vehicle stability prediction methods are mainly based on vehicle dynamics and physical modeling, which are insufficient to handle the complexities of real-world driving conditions. This paper explores the application of Long Short-Term Memory (LSTM) networks, which make them an ideal tool for stability modeling in dynamic environments. By comparing the performance of LSTM with traditional vehicle dynamics models, the advantages of deep learning in handling the complexity of real-time stability prediction are highlighted. This paper delves into how to integrate LSTM into prediction models to improve vehicle handling performance and expand its applicability to a wider range of driving conditions and vehicle types.