Digital Twin Modeling of a Highly Automated Agricultural Tractor

Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based testing offers scalability, existing approaches often lack seamless integration with real vehicles and field data, limiting their fidelity in capturing dynamic, real-world interactions. To bridge this gap, this paper proposes a novel real-time hybrid digital twin platform. Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link. A custom-developed middleware serves as the critical bridge, synchronizing a real CAV's kinematic state as a shadow vehicle in the simulation and translating virtual control commands into chassis-actuating Controller Area Network (CAN) messages for closed-loop control. Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation. Experimental results demonstrate stable synchronization and effective closed-loop control with low latency, confirming the platform's practicality for multi-scenario CAV verification.

We present a custom high-fidelity vehicle dynamics simulation environment for testing and validation of Autonomous Racing software. The digital twin of the autonomous vehicle is developed in Dymola, using racecar dynamics modeling libraries to build a complete multi-body model. A 3D road surface, including elevation profiles and curbs, is implemented using the Curved Regular Grid (CRG) standard. The model is exported from Dymola as a Functional Mock-up Unit (FMU) and integrated into a custom software-in-the-loop simulator, where communication interfaces with the autonomous racing stack were developed in C++. A calibration procedure based on experimental data is also presented, along with a validation study to further support the quality of the proposed framework. The simulator runs in real time on a portable computer and provides reliable ground truth for algorithms validation prior to real-world deployment.

This paper presents a novel non-linear mathematical model of an articulated tractor-trailer system that can be used, in combination with receding horizon techniques, to improve the performance of path tracking tasks of articulated systems. Due to its dual steering mechanisms, this type of vehicle can be very useful in precision agriculture, particularly for seeding, spraying and harvesting in small fields. The articulated tractor-trailer system model was embedded within a non-linear model predictive controller and the trailer position was monitored. When the kinematic of the trailer was considered, the deviation of trailer's position was reduced substantially alongside not only straight paths but also in headland turns. Using the proposed mathematical model, we were able to control the trailer's position itself rather than the tractor's position. The Robot Operating System (ROS) framework and Gazebo simulator were used to perform realistic simulations examples.