> For the complete documentation index, see [llms.txt](https://2025.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2025.istvs.org/submissions/papers/6001.md). # 6001 - MODELING AND SIMULATION OF AUTONOMOUS VEHICULAR SYSTEMS AT ERDC - II SOFT SOIL MODEL SUPPORT IN VEHICLE DYNAMICS {% hint style="info" %} Paper presented at ISTVS 2025 | 55th Conference of the International Society for Terrain-Vehicle Systems {% endhint %} **Authors:** *Peilin Song, Burhman Gates, Joshua Fairley* **Keywords:** Vehicle-Terrain Interaction; Traction; Sinkage; ProjectGL **Abstract:** In vehicular system development, ensuring the fidelity of vehicle dynamics models is critical, especially for military vehicles operating primarily on off-road terrains, where mobility is significantly influenced by various types of soft soils. The U.S. Army Engineer Research and Development Center (ERDC) has been a pioneer in off-road mobility research, conducting extensive testing on various vehicles across different terrains. These tests have led to the development of empirical formulations for traction, resistance, and sinkage of tractive elements, such as tires and tracks. ERDC/GSL's Vehicle-Terrain Interaction (VTI) API, which covers both wheeled and tracked vehicles, provides a comprehensive set of empirical relationships that simplify the modeling of vehicle-terrain interactions. These relationships streamline the modeling process, simplify the overall vehicle models, and significantly enhance simulation fidelity. This paper discusses the integration of the VTI API as a plugin within Unreal Engine and ProjectGL, aiming to improve the accuracy and realism of vehicle mobility simulations. By incorporating VTI into these platforms, we achieve high-fidelity simulations of vehicle performance on soft soils, thereby advancing the modeling and analysis of military vehicle mobility in off-road conditions. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://2025.istvs.org/submissions/papers/6001.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.