Leveraging LSTM Networks for Vehicle Stability Prediction: A Comparative Analysis with Traditional Models under Dynamic Load Conditions
DOI:
https://doi.org/10.64229/rmaw9551Keywords:
Vehicle Stability, LSTM, Deep Learning, Time-Series Analysis, Load Variability, Machine Learning, Dynamic Driving Conditions, Vehicle Dynamics, Predictive Modeling, Real-time SystemsAbstract
Vehicle stability, particularly under dynamic vertical load conditions, is a critical factor in automotive safety and performance. Traditional methods, primarily based on vehicle dynamics and physical modeling, often fail to address the non-linearities and complexities inherent in real-world driving conditions. This paper explores the application of Long Short-Term Memory (LSTM) networks, a deep learning model designed for time-series data, to predict vehicle stability under varying loading conditions. LSTM's ability to capture temporal dependencies and non-linear relationships makes it a promising tool for modeling stability in dynamic environments. By comparing the performance of LSTM with traditional vehicle dynamics models, this study highlights the advantages of deep learning in handling the complexities of real-time stability prediction. Using a comprehensive dataset that includes variables such as load, vehicle speed, and road conditions, the results indicate that LSTM models outperform traditional methods in capturing the intricate dynamics of vehicle behavior, particularly under fluctuating loads and changing road conditions. However, challenges related to model interpretability, computational demands, and data quality persist, suggesting that further research is needed to optimize LSTM's application in real-time stability systems. This study contributes to the growing body of research on the application of machine learning in automotive safety, providing insights into how LSTM can be integrated into predictive models for improved vehicle control. Future work could focus on refining model accuracy and expanding its applicability to a broader range of driving conditions and vehicles.
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