Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks

2026-03-18

AI Summary

This work addresses the limitations of conventional modeling approaches for distributed electric-drive trucks, which struggle with efficiency, accuracy, and compatibility with linear control due to strong nonlinearities and longitudinal–lateral coupling. To overcome these challenges, the authors propose a fully data-driven dynamics modeling framework grounded in Koopman operator theory. By employing a dual-branch encoder architecture and a geometry-consistent physics-informed supervision mechanism, the method maps the nonlinear system into a linear embedding space. A hybrid Koopman framework is further introduced to accommodate multiple driving modes. Validated through high-fidelity TruckSim simulations and real-vehicle experiments, the approach achieves state-of-the-art long-term state estimation accuracy while maintaining high precision, interpretability, and control-friendly properties.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Advanced autonomous driving systems require accurate vehicle dynamics modeling. However, identifying a precise dynamics model remains challenging due to strong nonlinearities and the coupled longitudinal and lateral dynamic characteristics. Previous research has employed physics-based analytical models or neural networks to construct vehicle dynamics representations. Nevertheless, these approaches often struggle to simultaneously achieve satisfactory performance in terms of system identification efficiency, modeling accuracy, and compatibility with linear control strategies. In this paper, we propose a fully data-driven dynamics modeling method tailored for complex distributed electric-drive trucks (DETs), leveraging Koopman operator theory to represent highly nonlinear dynamics in a lifted linear embedding space. To achieve high-precision modeling, we first propose a novel dual-branch encoder which encodes dynamic states and provides a powerful basis for the proposed Koopman-based methods entitled KODE. A physics-informed supervision mechanism, grounded in the geometric consistency of temporal vehicle motion, is incorporated into the training process to facilitate effective learning of both the encoder and the Koopman operator. Furthermore, to accommodate the diverse driving patterns of DETs, we extend the vanilla Koopman operator to a mixture-of-Koopman operator framework, enhancing modeling capability. Simulations conducted in a high-fidelity TruckSim environment and real-world experiments demonstrate that the proposed approach achieves state-of-the-art performance in long-term dynamics state estimation.

Problem

• vehicle dynamics modeling
• nonlinear dynamics
• distributed electric-drive trucks
• system identification
• longitudinal and lateral coupling

Innovation

• Koopman operator
• dual-branch encoder
• physics-informed learning
• mixture-of-experts
• distributed electric-drive trucks