  Resolving edge-cases in autonomous driving, head-to-head autonomous racing is
getting a lot of attention from the industry and academia. In this study, we
propose a game-theoretic model predictive control (MPC) approach for
head-to-head autonomous racing and data-driven model identification method. For
the practical estimation of nonlinear model parameters, we adopted the
hyperband algorithm, which is used for neural model training in machine
learning. The proposed controller comprises three modules: 1) game-based
opponents' trajectory predictor, 2) high-level race strategy planner, and 3)
MPC-based low-level controller. The game-based predictor was designed to
predict the future trajectories of competitors. Based on the prediction
results, the high-level race strategy planner plans several behaviors to
respond to various race circumstances. Finally, the MPC-based controller
computes the optimal control commands to follow the trajectories. The proposed
approach was validated under various racing circumstances in an official
simulator of the Indy Autonomous Challenge. The experimental results show that
the proposed method can effectively overtake competitors, while driving through
the track as quickly as possible without collisions.
