MAP2: Model- and Acceleration-Based Pursuit with MPC and Gaussian Process Residual Learning for Autonomous Racing
MAP2 is a lightweight controller for autonomous racing that combines Model Predictive Control (MPC) with Sparse Gaussian Process (SGP) residual learning. It uses MPC to predict desired lateral acceleration and then uses SGP to correct errors between the model and real-world tire dynamics, significantly improving tracking accuracy and robustness against model mismatches compared to existing methods.