LLM-guided scenario-adaptive lateral organization and learning-assisted predictive control for automated truck platoons

The package supports reproduction of the main numerical results reported in the study, including the MATLAB/Simulink simulations of a heterogeneous five-truck platoon, the longitudinal model predictive control with bounded RBF residual compensation, the LLM-guided scenario interpretation and deterministic validation process, and the scenario-adaptive lateral organization optimization. The package includes MATLAB/Simulink model files, MATLAB scripts for lateral organization optimization and figure generation, Python scripts for LLM-guided scenario interpretation and safety validation, scenario input and output files, trained RBF model files, processed simulation data, and reference output figures/tables. The simulated scenarios are generic road and traffic segments and are not based on proprietary, confidential, or human-subject data. Detailed instructions for reproducing the main results are provided in the README file and the replication explanatory file. The simulated scenarios are generic road and traffic segments and are not based on proprietary, confidential, or human-subject data.