An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars

TL;DR

This paper introduces an inverse optimal control method for predicting autonomous race car trajectories, combining optimization, learning, and real-world implementation to improve accuracy and interpretability with limited data.

Contribution

It presents a novel inverse optimal control algorithm that predicts vehicle trajectories by learning parameters from observed data, suitable for real-time embedded systems.

Findings

Accurate trajectory prediction with sparse data.

Successful implementation on real race car hardware.

Effective learning of vehicle behavior parameters.

Abstract

This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.