Koopman Operator Approach Data-Driven Optimal Control Algorithm for Autonomous Vehicles with various characteristics

The complex mathematical model of autonomous vehicles makes it difficult for system identification due to a combination of non-linearity and uncertainty. Various strategies have been proposed to address the difficulty in system identification, as it significantly influences the precise path-tracking performance of autonomous vehicles. This paper proposes a Koopman operator approach data-driven optimal control algorithm for path-tracking of autonomous vehicles. To identify mathematical model's various vehicle types of autonomous vehicle driving data were acquired in virtual simulation and real-world environments. An integrated linear model was identified using the Koopman operator neural network and the acquired driving data of autonomous vehicles. The identified integrated linear model was incorporated into a model predictive control algorithm designed for the path-tracking of autonomous vehicles. Reasonable path tracking performance was confirmed through performance evaluations conducted in path-tracking scenarios using various vehicle types for real and virtual vehicles in the real autonomous driving proving ground C-track and CARLA simulator environments.