A Gaussian Process Model for Opponent Prediction in Autonomous Racing

In head-to-head racing, performing tightly con-strained, but highly rewarding maneuvers, such as overtaking, require an accurate model of interactive behavior of the opposing target vehicle (TV). We propose to construct a prediction model given data of the TV from previous races. In particular, a one-step Gaussian process (GP) model is trained on closed-loop interaction data to learn the behavior of a TV driven by an unknown policy. Predictions of the nominal trajectory and associated uncertainty are rolled out via a sampling-based approach and are used in a model predictive control (MPC) policy for the ego vehicle in order to intelligently trade-off between safety and performance when racing against a TV. In a Monte Carlo study, we compare the GP-based predictor in closed-loop with the MPC policy against several predictors from literature and observe that the GP-based predictor achieves similar win rates while maintaining safety in up to 3x more races. Through experiments, we demonstrate the approach in real-time on a 1/10th scale racecar platform operating at speeds of around 2.8 m/s, and show a significant level of improvement when using the GP-based predictor over a baseline MPC predictor. Videos of the experiments can be found at https://voutu.be/KMSs4ofDfIs.