基于学习的建模和自动驾驶和人工驾驶车辆的有效控制Mixed-Traffic-GP-MPC附matlab代码.zip

## Mixed-Vehicle Platooning Control with a Gaussian Process Learning-Based Model This repository hosts the code accompanying our research on enhancing safety in mixed-traffic platooning, which involves both autonomous and human-driven vehicles. Our work is detailed in the paper: * [Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles](https://arxiv.org/abs/2211.04665) ### Please check our preliminary work at [GP-MPC-of-Platooning](https://github.com/CL2-UWaterloo/GP-MPC-of-Platooning). ### ARX_simulation This directory contains code for simulating a vehicle platoon using Model Predictive Control (MPC) with an AutoRegressive with eXogenous inputs (ARX) model combined with a Gaussian Process (GP). It includes: * `MPC_platoon_simulation.m`: Main simulation script. * `MPC_platoon.m`: Implementation of the MPC. * `GP_sysmodel.m`: Vehicle dynamics model for simulations. * `gpr_medium.mat`: Standard GP model trained on field data. * `gpCallback.m`: Callback function for CasADi to integrate the GP model (ARX+GP) * `wltp_velocity_profile.m`: Script for the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) velocity profile. ### GP_ARX_simulation This folder contains the code for simulating a platoon of vehicles using MPC with a sparseGP+ARX model, while the simulated vehicle is a GP+ARX model. Specifically, the following files are included: * `GP_MPC_platoon_simulation.m`: Main script for sparseGP-MPC-based simulations. * `GP_MPC_platoon.m`: Gaussian Process Model Predictive Control (GP-MPC) implementation. * `GP_sysmodel.m`: Vehicle dynamics model for simulations. * `gpr_medium.mat`: Standard GP model trained on field data. * `gpr_sparse.mat`: Sparse GP model, trained using `GP_RE_trainForSpares.m` in the `GP_training` folder. * `gpCallback.m`: Callback for CasADi, loading the sparseGP+ARX model for control purposes. * `gpCallback_sys.m`: Callback for CasADi, integrating the GP+ARX model for simulating autonomous vehicles. * `wltp_velocity_profile.m`: WLTP velocity profile script. ### GP_training Contains scripts for training Gaussian Process (GP) models: * `GP_training`: Script for training standard GP models. * `GP_RE_trainForSpares`: Script for developing sparse GP models. * `plot_to_pdf`: Utility to export plot visualizations as PDFs.