Physics-Informed LSTM: This workflow follows the Universal Differential Equation methodology via the incorporation of stochastic modeling, the Intelligent Driving Model, with an LSTM structure in a joint-training paradigm for predicting human driving behavior.
Note: This repository reflects the workflow of Alex Glover's Master of Science in Computer Science: Machine Learning and AI thesis at East Tennessee State University, accepted March of 2025
Sequence Handling: Truncated sequences, Padded & Masked sequences
Intelligent Driving Model: Linear, Nonlinear
LSTM Cell Count
Float Precision
Random Seed Value: NumPy, Scikit-Learn, TensorFlow
Training-Only Behaviors
Training-Validation Behaviors
Testing Final Values