Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame

• URL: http://arxiv.org/abs/2503.03002v1
• Date: Tue, 04 Mar 2025 20:57:38 GMT
• Title: Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame
• Authors: Mohammad Abtahi, Mahdis Rabbani, Armin Abdolmohammadi, Shima Nazari,
• Abstract summary: This paper introduces a novel deep learning-based Koopman modeling approach that employs deep neural networks to capture the full vehicle dynamics.<n>The superior accuracy of the Koopman model compared to identified linear models is shown for a double lane change maneuver.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal and Model Predictive Control (MPC) approaches in path planning and following. Koopman operator theory offers a global linear representation of nonlinear dynamical systems, making it a promising framework for optimization-based vehicle control. This paper introduces a novel deep learning-based Koopman modeling approach that employs deep neural networks to capture the full vehicle dynamics-from pedal and steering inputs to chassis states-within a curvilinear Frenet frame. The superior accuracy of the Koopman model compared to identified linear models is shown for a double lane change maneuver. Furthermore, it is shown that an MPC controller deploying the Koopman model provides significantly improved performance while maintaining computational efficiency comparable to a linear MPC.

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