Learning-Based Predictive Control Method for Vehicle Lateral Control with a Multi-Step Gaussian Process Regression Prediction
The work introduces a novel Model Predictive Control method for vehicle lateral control that uses multi-step Gaussian Process Regression to predict uncertainties over time. This approach captures state- and control-dependent errors more accurately than single-step methods, allowing the controller to make safer decisions by anticipating how modeling errors accumulate during maneuvers.