A Gaussian Process Model for Opponent Prediction in Autonomous Racing

by   Finn Lukas Busch, et al.

In head-to-head racing, performing tightly constrained, but highly rewarding maneuvers, such as overtaking, require an accurate model of interactive behavior of the opposing target vehicle (TV). However, such information is not typically made available in competitive scenarios, we therefore propose to construct a prediction and uncertainty 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 attempting overtaking maneuvers against a TV. We demonstrate the GP-based predictor in closed loop with the MPC policy in simulation races and compare its performance against several predictors from literature. In a Monte Carlo study, we observe that the GP-based predictor achieves similar win rates while maintaining safety in up to 3x more races. We finally demonstrate the prediction and control framework in real-time on hardware experiments.


Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Enhancing Safety in Mixed-Vehicle Platooning Control

As autonomous vehicles (AVs) become more prevalent on public roads, they...

KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots

In this work, we consider the problem of deriving and incorporating accu...

Gaussian Process Learning-Based Model Predictive Control for Safe Interactions of a Platoon of Autonomous and Human-Driven Vehicles

With the continued integration of autonomous vehicles (AVs) into public ...

Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

A fundamental aspect of racing is overtaking other race cars. Whereas pr...

Computationally Efficient Data-Driven MPC for Agile Quadrotor Flight

This paper develops computationally efficient data-driven model predicti...

Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories

We present a novel predictive model architecture based on the principles...

Nonlinear MPC for Quadrotors in Close-Proximity Flight with Neural Network Downwash Prediction

Swarm aerial robots are required to maintain close proximity to successf...

Please sign up or login with your details

Forgot password? Click here to reset