ABSTRACT. In vehicle dynamics, the measurement of accurate vehicle velocity (magnitude and direction) using low-cost sensors is a significant challenge, yet this measurement is extremely valuable as an input for vehicle control systems. Traditional low-cost methods for measuring vehicle velocity often rely on dual antenna GPS algorithms that have reduced accuracy at low speed and GPS reception is often challenging in varied environments. This work applies an optical technique to measure the longitudinal and lateral velocities required to calculate slip using a downward facing depth camera (Intel Realsense) to capture images and a point cloud of the terrain surface. The images and point cloud are processed by a feature tracking algorithm and the vehicle velocity is calculated based on the relative motion of the ground and vehicle body. The technique is applied to a four-wheeled, skid steer, off-road, unmanned ground vehicle (UGV) that frequently encounters low-traction conditions while operating on slippery and uneven terrain. This suspensionless UGV has electric servo motors driving each wheel independently, allowing both measurement and control of wheel speed and torque. The calculated vehicle velocity is combined with wheel speeds from the motors to calculate individual longitudinal wheel slips. The measurement technique’s performance is evaluated by using the vehicle velocity and longitudinal wheel slip in a control algorithm that is designed to maintain a desired vehicle attitude even when wheel slip occurs. The slip measurement technique and control algorithm are evaluated by driving the vehicle on low friction and split friction surfaces while recording the desired attitude and actual vehicle attitude.

ABSTRACT. How can we make autonomous vehicles smarter in unpredictable, off-road environments? In this talk, I will share recent progress on using Koopman theory as a powerful tool for data-driven autonomy. By transforming complex, nonlinear vehicle dynamics into a linear framework, Koopman theory allows us to build linear models directly from data. This linear structure then makes it possible to design adaptive control strategies that can handle the challenges of off-road navigation with greater reliability and flexibility.

ABSTRACT. Autonomous unmanned ground vehicles are expected to be utilized at disaster sites. The previously obtained map information for the site is often unreliable because disaster areas can change rapidly and unpredictably. Therefore, the vehicles must be able to recognize the surrounding rough terrain environment in real time and plan optimal routes to approach the destination safely. However, local path planning may fall into the local minimum solution and get stuck. Therefore, in this study, a method of avoiding a standstill by utilizing information entropy and route history to encourage exploratory behavior was investigated. In the proposed method, routes are selected by determining the vehicle behavior using reinforcement learning. For observation information, a Digital Elevation Model, an information entropy map of elevation, and a map that records the number of times the vehicle has traveled on the grid, in addition to the vehicle's state were used. The aim is to acquire the behavior of going to the target with selecting a safe route to avoid getting stuck by rewarding the vehicle for going to the target, avoiding uneven terrain, reducing information entropy, and not repeatedly driving in the same place. In this study, we tested the proposed method on each terrain with a single large obstacle and a dead-end by simulations. The simulation results showed that the vehicle can reach the target efficiently by introducing rewards for exploratory behavior.

ABSTRACT. Unmanned Ground Vehicles (UGVs) are increasingly deployed in environments where human access is limited or hazardous, such as military operations, disaster response, and remote exploration. The autonomous or semi-autonomous operation of these platforms relies heavily on advanced perception systems, which integrate various sensors, including LiDAR, radar, cameras, and inertial measurement units (IMUs). However, complex environments—especially those with dense vegetation—pose significant challenges for object detection and navigation. Occlusion, signal scattering, and distortion from foliage significantly reduce sensor effectiveness, compromising mobility and operational safety. This study analyzes the limitations of both active sensors (LiDAR, radar) and passive sensors (cameras) under adverse environmental conditions, particularly in contexts where security and reliability are critical. Special attention is given to the UGV’s ability to traverse obstacles in off-road terrain, focusing on design constraints and mechanical limitations that affect mobility. Additionally, the challenges in detecting and classifying objects hidden in dense vegetation are addressed to avoid collisions and prevent vehicle damage. Object recognition based on physical properties is emphasized, especially in autonomous navigation contexts. These findings are particularly relevant to sensor systems, which provide critical data to the vehicle’s navigation subsystem in unmanned mode. The study underscores the importance of optimizing sensor fusion and vehicle design to enhance reliability and mobility in challenging operational environments.

ABSTRACT. Vehicle fording can be difficult to assess because of numerous requirements to ensure safe crossing based on the vehicle and the changing river characteristics. While some of these requirements are well-known, others are so complex and variable that they remain ill-defined. This paper reviews river crossing guidance with emphasis on the river and riverbed characteristics. While seasonal changes in flow rate are somewhat predictable with hydrologic models, they rely on many assumptions about the river bathymetry and discharge. The riverbank conditions can also change drastically with season, affecting ingress and egress. Lastly, the competence of the river bottom sediments (strength and stability) is extremely difficult to estimate or measure, so this was a primary focus for this research.

Recent progress in fluvial geomorphology and a new field called river morphodynamics may help in determining the best places to cross a river based on river type, processes, and adjacent landforms. Equations from fluvial geomorphology and geotechnical engineering are used to relate river flow conditions to bottom sediment type and strength. Exploratory field measurements of river bottom strength were performed. A methodology to evaluate the strength of the river bottom sediments was developed and results using a modified static cone penetrometer were analyzed.

This research represents a major step forward in understanding and predicting suitable river bottoms for fording, presents a methodology to measure strength in river sediments, and highlights areas of future research needs. Applicable fording guidance, geomorphology, and recent advancements in terramechanics as related to river crossings are summarized to provide context.

ABSTRACT. Military training areas are unique in their function and usage. These areas must endure intensive and repeated movement of wheeled and tracked military vehicles, as well as dismounted troops. Depending on the training requirements of defense forces, military training areas can undergo rapid land-use changes. For instance, when new firing ranges or maneuvering grounds are needed, forests may be cleared to create open areas. Deforestation has a significant impact on soil properties. Logging and uprooting activities displace soil layers and alter soil structure. The primary goal of training land managers is to restore the usability of deforested areas as quickly as possible, making the restoration of disturbed ground a key focus. The objective of this study was to assess the status of soil physical properties, chemical composition, and trafficability conditions two years after deforestation. As deforestation contributes to the release of carbon dioxide (CO₂) into the atmosphere, it plays a significant role in climate change. A key objective of this study was to evaluate changes in soil CO₂ levels following deforestation. Measurements were conducted in 2020 at a military training ground in northern Estonia. The study included bulk density and moisture content measurements, as well as the determination of California Bearing Ratio (CBR) and Cone Index (CI) values for both disturbed and undisturbed areas. The plant-available nutrient content (P, K, Mg, Ca) was determined using the ammonium acetate-lactate (AL) method. Soil carbon and nitrogen content were measured using the Dumas method. Measurement results are presented, highlighting the effects of disturbance and recovery on soil properties.

ABSTRACT. Laboratory and field studies were conducted over three years on fine grained and peat soils measuring the effects of soil properties on vehicle performance, namely drawbar pull, and motion resistance. Tests were conducted for unfrozen, frozen, and thawed states using eight different vehicles both wheeled and tracked. In the laboratory, soil moisture and temperature could be controlled. In both the field and laboratory, the soils were tilled then smoothed before testing. The soil was characterized pre and post testing using a variety of strength instruments. Rut depths associated with motion resistance tests were recorded. This paper discusses the effects of measured soil properties on rut depth as a function of soil state as well as compares results to existing algorithms. Soil properties measured using different techniques are also compared.

ABSTRACT. The Keweenaw Research Center (KRC) at Michigan Tech University has designed and built an automated Bevameter to measure in-situ soil strength and derive Bekker-Wong mobility parameters. Testing has been performed in a number of soils including high organic soils (peat), silts, sands, gravels, and lunar regolith at various moisture contents. A data set containing derived strength parameters is being built and results that are non-proprietary and non-controlled are shared with permission on the KRC website.

KRC has also performed several vehicle mobility tests both in the field and in the lab on the KRC single wheel tire tester. A large amount of this collected data is also available on the website with permission. All testing includes cone penetrometer, nuclear density, moisture, and surface friction when possible.

This paper will be an overview of the Bevameter and the data collected to date, as well as an overview of other mobility testing that has been performed.

ABSTRACT. Unmanned Ground Vehicles (UGVs) are becoming critical assets for the Army, offering enhanced capabilities for mission execution. However, the complexity of autonomous systems presents challenges that traditional analysis tools are ill-equipped to handle. To address this, ERDC has developed a Software Integration Laboratory (SIL) to support the modeling and simulation (M&S) of UGVs in virtual environments. The SIL provides a digital testbed for performance prediction and risk mitigation across both typical and edge-case scenarios. As part of ongoing test and evaluation (T&E) endeavors, ERDC’s SIL supports a wide range of activities that complement physical field trials. This includes the creation of high-fidelity virtual environments that integrate vehicle dynamics, terramechanics, terrain models, and sensor simulations. These environments enable realistic testing of autonomous navigation systems—such as the Robotic Technology Kit (RTK) and its variants—in off-road and contested conditions. This paper highlights the design and capabilities of ERDC’s SIL and its application to autonomous ground vehicle testing and development. It demonstrates how digital experimentation using the SIL supports the integration of autonomous systems into manned-unmanned teams and helps inform future mission planning. The paper also presents the application of the SIL in various projects, including both single autonomous vehicle operations and the coordinated motion of multiple vehicles in team-based scenarios.

ABSTRACT. Modeling and simulation (M&S) of autonomous vehicles (AVs) for military applications requires rigorous verification and validation (V&V) processes to ensure the reliability and safety of these systems in complex, dynamic environments. Only properly verified and validated, the Software Integrated Laboratory (SIL) can be used effectively in broader operational scenarios. Once a model that is properly verified and validated in normal cases can be extended to edge cases such as raining, fogging, and dusty conditions. This paper presents an overview of the V&V efforts of SIL at the U.S. Army Engineer Research and Development Center (ERDC) to simulate autonomous vehicles. As the SIL is hierarchical in nature, we developed a corresponding hierarchical methodology for the V&V of the SIL across different mission types. This methodology ensures that V&V is performed at the component level before being elevated to the system level, providing a structured approach to ensure the accuracy and reliability of the simulations. The case study presented in this paper demonstrates the effectiveness and usefulness of the V&V process developed by ERDC in validating autonomous vehicle systems. The paper discusses the frameworks and processes used to verify and validate these systems, highlighting the challenges and insights gained from applying V&V practices to autonomous vehicle development.

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.

ABSTRACT. The sensors used by a robust, off-road autonomous vehicle must be capable of detecting obstacles in any weather condition, but this becomes especially important during inclement weather with reduced visibility. Snowfall is no exception to this and presents extremely challenging conditions to vehicles exclusively employing Light Detection and Ranging (LiDAR) systems for object detection. Snowflakes may occlude “hard” targets in the vehicle’s path of travel or introduce noise from “soft” detections of the snowflakes themselves. Since it is challenging to acquire experimental data for all possible snowfall conditions, numerical simulations are often employed to predict sensor performance. This study highlights preliminary results from a simulation that reproduces realistic snowfall effects within a Virtual Autonomous Navigation Environment (VANE). The simulation models snowflake size and concentration while accounting for sensor-specific modes of operation of the Velodyne HDL-32 LiDAR. VANE simulations provide vehicle designers with a rapid and affordable method to evaluate LiDAR perception and autonomy response in snowy conditions.

ABSTRACT. Autonomous vehicles can experience extreme changes in performance when operating over winter surfaces, and require accurate classification to transit them safely. In this work we consider acoustic classification of winter terrain, and demonstrate that a simple and efficient frequency-space analysis exposed to a small convolutional neural network, rather than recurrent architectures or temporally-varying spectrogram inputs, is sufficient to provide near-perfect classification of deep snow, hardpacked surfaces and ice. Using a dual-microphone configuration, we also show that acoustic classification performance is due to a combination of vehicle noises and vehicle-terrain interaction noises, and that engine sounds can serve as a particularly powerful classification cue for offroad environments.

ABSTRACT. Military operations rarely occur in a single terrain—missions often transition from paved highways to rugged trails, unimproved roads, or entirely off-road environments. Building separate autonomy systems for each creates gaps, integration challenges, and operational risk. Kodiak takes a different approach: one AI driver that seamlessly covers all operating domains, meeting the demands of military ground operations while adhering to the law of the road, civilian traffic, safety, and rules of engagement.

Kodiak is the leading provider of dual-use ground vehicle autonomy and the first to field a driverless product owned and operated by a customer. Our mission is to deliver a reliable, flexible human driver replacement for on-road and off-road operations. Since 2018, Kodiak has deployed the Kodiak Driver across commercial and DoD missions. From 2022–2024, we competed in the Army’s Robotic Combat Vehicle program, integrating our system into Ford F-150s to demonstrate terrain-aware off-road navigation, secure teleoperation, and rapid adaptation to new Environments. In 2024, we integrated onto the Textron M3 Ripsaw, proving our platform-agnostic design.

Our modular, vehicle-agnostic autonomy stack integrates across platforms, supports high uptime with rapid hardware replacement, and is built on a Modular Open Systems Approach (MoSA) for mission command, teleoperation, and communications. One AI driver across all domains ensures mission continuity, reduces complexity, and enables safer operations across the battle space.

ABSTRACT. To ensure safe and robust autonomous offroad-operation, specific challenges must be managed, including robust terrain-specific obstacle detection. Virtual approaches have the potential to significantly shorten development time and effort. Adequate methods need to consider changing environmental conditions, as e.g. changes of the vegetation during the seasons of the year as well as weather. To manage all those requirements, a modular simulation platform has been developed.

The toolchain consists of six independent and flexible modules, which are all linked together to conduct simulations seamlessly and with best correlation to real world:

1. Vehicle-specific multibody dynamic simulation module of the vehicle, which can be derived from different multibody dynamic simulation tools, whereby a mathematical soft-soil model applies,

2. A terrain model, which utilizes a comprehensive set of specifically designed terrain elements, and which undergo AI-supported modifications, e.g. darkness, fog or rain during the validation process,

3. A sensor module, which can operate with different kinds of active and non-emitting sensors. Sensor selection and arrangement on the vehicle are optimized in the virtual sensor lab,

4. An autonomous algorithm, which is the system-under-test (SuT) and which gets optimized and validated,

5. Set of terrain-specific KPI’s, which define application-specific boundaries and qualification criteria,

6. A terrain-specific scenario database, where ontology-based automated offroad-scenarios are generated.

The sensor model and SuT get integrated into the Vehicle model, and the vehicle is executing the automatically generated, ontology-based testcase-combinations. Application-specific KPI’s are calculated and compared with target values.

ABSTRACT. Understanding effects of electronic control systems, such as Antilock Braking System (ABS) and Electronic Stability Control (ESC), and their interaction with the steering control algorithm is critical for improving mobility and maneuverability of autonomous wheeled vehicles in off-road environments. In this work, we evaluate effects of wheel-speed based ABS and yaw-rate-following ESC on the performance of autonomously driven Polaris RZR vehicle performing a double-lane-change (DLC) maneuver in granular terrain by identification of maximum DLC passing speeds. Two lateral speed controllers are compared: simple PID and Stanley autonomous steering controller, along with the longitudinal speed controller that accelerates the vehicle to identify maximum DLC passing speed. The simulations were conducted using Chrono simulation package with the Soil Contact Model (SCM) parameters. The SCM parameters were calibrated to represent dry sand by using data from published experimental measurements. The ABS effects were found to be negligible or detrimental, while ESC augmented the lateral steering control algorithm allowing the vehicle to achieve higher DLC passing speeds.

ABSTRACT. A wide variety of software packages are available for modeling Light Detection and Ranging (LiDAR) sensors in the context of autonomous driving simulations. This report highlights ten effects that a high-fidelity LiDAR simulation should be capable of delivering. Examples are provided from real-world data collected in the field, synthetic results generated in simulation, and published literature. Simulation users must carefully consider performance versus fidelity tradeoffs when selecting a suitable LiDAR simulation package for use

ABSTRACT. In any vehicle dynamics simulation model, the tyre model is of upmost importance as it is the only component in contact with the terrain. In many cases engineers under estimate the contribution the tyre model makes to the reliability and accuracy of the simulation results. On larger off-road vehicles this is the case due to the limited tyre data and models available to the engineers as parameterize tyres are not trivial and can be very expensive compare to the cost of the tyre. On very large tyres the infrastructure required to conduct tyre parameterization tests are not available. Vehicles used on soft terrain are also often used on hard terrain. Tyre parameters are measured on hard terrain as the tyre carcass construction, inflation pressure and vertical load on the tyre dictates the linear section of the longitudinal and lateral tyre parameters. The maximum longitudinal and lateral load generated by the tyre is dictated by the surface roughness of the terrain over which the tyre transverse. In this study we conduct in-situ test on 2 vehicles on a specific surface. These results are compared to laboratory tests conducted on the same tyres and same surface. This method enables engineers to obtain first order tyre parameters to validate simulation models with limited and non-specialized equipment.

ABSTRACT. This paper introduces a novel approach for investigating the interaction between a passenger car tire and salted pavement. A passenger car tire size 235/55 R19 was modelled within a Finite Element Analysis (FEA) software called Virtual Performance Solution (VPS) using 28 layers including solid, layered membrane, beam and shell elements. The modelled tire was validated in static and dynamic domains using manufacturer-provided data. The salt used to sit on top of the pavement was modelled using Smoothed-Particle Hydrodynamics (SPH) techniques and calibrated using shear-strength test. The salted pavement–tire interaction required the use of node-to-segment contact algorithm with edge treatment to ensure no penetration between the salt particles and the tire elements. The focus of this paper is to investigate the tractive efforts of the driven tire over the salted pavement under different operating conditions. The operating conditions include vertical loads of 3.5 kN, 5 kN, 8 kN and a nominal inflation pressure of 228 kPa at constant angular velocities equivalent to longitudinal speeds of 10 kph, 50 kph and 100 kph. To understand the impact salt has on the driving conditions of icy roads several tests including a 57 mm, 114 mm and 171 mm salt depth was selected to understand the impacts of the amount of salt on a surface. This study provides insight to the use of road salt in the winter months in Canada. As road salt is crucial in winter climates it is important to understand the implications that salting pavement will have on the performance of passenger car tires through advanced computational techniques. By understanding the implication salt has on different pavements affected by winter a deeper understanding of safety, and tire traction performance can be achieved.

ABSTRACT. Vehicle stability is inherently related to tyre-terrain interaction. Slip conditions arising from tyre forces generated due to the terrain and vehicles state is a valuable indication of a vehicle’s stability. Measurement and analysis of the tyre contact patch is complex, yet abundant information can be derived from contact patch measurements. Literature indicates that analysis of the contact patch is difficult, and indirect methods are used to determine tyre states under ideal conditions of paved roads. Few researchers attempt to directly analyse the contact patch and extract key tyre states: slip angle, pressure distribution, terrain deformation, contact patch velocity and dynamic rolling radius. This study uses the T2Cam apparatus, a mechanism that fits into a tyre and provides the capability to attach cameras inside the wheel that remain stationary despite the tyre rotating. The use of T2Cam and an RGBD camera provides the capability of a non-contact method using DIC (Digital Image Correlation) to measure tyre contact patch deformation and relate that to key tyre states. The purpose of this study is to develop algorithms to extract key states from contact patch deformation of the inside of the tyre carcass under varying tyre conditions. Preliminary findings show tyre deformation of the contact patch is measurable and accurate to be used as an input in an algorithm to calculate slip angle, contact patch velocity and dynamic rolling radius. Overall, determining these factors will enhance the understanding and visualisation of the dynamic contact patch and prediction of vehicle stability on many types of terrain.

ABSTRACT. A heavy vehicle operating near a steep slope alters the stress distribution and might cause the slope to collapse. The result can be catastrophic for the operator, the machine, and for other units in the area. Computational methods for assessing the risk for slope collapse has existed for a long time in geotechnics but are limited to static conditions without mobile equipment.

We explore a contact dynamics approach to slope failure analysis. The method searches a space of potential failure surfaces for the most critical one. The soil in each potential failure surface is treated as a rigid body supported by contact forces with the interfacing (static) terrain. The body is subject to gravity and contact forces from any multibody systems, such as a vehicle, interacting with it. A Signorini-Mohr-Coulomb contact model is assumed, stating that the relative normal and tangential velocity should be zero along the interface unless the normal and tangential forces reach their limit conditions, that depends on the (internal) friction and cohesion parameters, in which case the body is prone to acceleration. The discretized equations of motion, constrained by the contact model, form a linear complementarity problem that is solved numerically. The solution reveals whether the configuration is unstable or stable, and is factor-of-safety (FOS). A search algorithm for finding the most critical failure surface, in terms of smallest FOS or highest acceleration, is developed and analysed. The method was implemented in the realtime physics engine AGX Dynamics. The tests shows that it gives reliable results, comparable with conventional LEM methods, and is computational efficient enough for being used with a realtime simulator of terrain vehicles.

ABSTRACT. The Bekker-Wong terramechanics model remains a cornerstone for predicting wheel–soil interactions, relying on Bevameter tests to characterize soil behavior via plate sinkage and in situ shear tests. However, accurately modeling these tests, and the underlying physics, remains challenging. Physics-based methods like the Discrete Element Method (DEM) lack key critical state soil mechanics features such as density dependence, yield functions, and pore pressure effects. This study employs an implicit, large-strain Finite Element Method (FEM) formulation and the NorSand critical state model to simulate plate sinkage and shear testing in partially saturated Cullinan sand. The advanced NorSand model captures soil behavior through evolving yield surfaces and stress–dilatancy relationships linked to effective stress, void ratio, and density-dependent soil strength. Model parameters were calibrated using 18 CU and CD triaxial tests via a novel, automated Bayesian optimization scheme, with calibration accuracy enhanced by empirical meta-models for the shear modulus G and hardening parameter H. Validation against field Bevameter experiments was conducted using a forward-only modeling approach. The model shows good agreement with in situ shear tests (6–15% relative error in the pre-failure region) and captures peak shear strength and post-peak softening trends across a range of densities. Plate sinkage predictions match early stage stiffness (16% error at intermediate density) but diverge at large settlements due to numerical instability. This study highlights how physics-based methods, grounded in critical state soil mechanics, can bridge the gap between laboratory calibration and reliable field predictions.

ABSTRACT. We present models and numerical methods for realtime and faster simulation of wheeled and tracked vehicles traversing and interacting with deformable terrain. The wheel-terrain interaction is formulated as a set of kinematic constraints with constraint forces and limits that reflect the stresses on the wheel and soil failure at critical stresses. When soil failure occurs, a 3D soil displacement field is predicted and used to update the spatial distribution of soil, local packing density, and the surface heightmap. The constraint-based formulation enables stable and strong coupling between the terrain and vehicle multibody dynamics at large time-steps. Simulation tests are performed where the implemented model is compared to experiments and standard semi-empirical terramechanics models. Finally, we apply the terramechanics model on a model of a heavy forest machine traversing rough terrain and study how the motion and dynamic load forces are affected by inclusion of deformable terrain.

ABSTRACT. In spite of advances in vehicle dynamics modeling, accurately simulating off-road vehicle performance on deformable terrains remains computationally challenging. In this paper, we develop an efficient yet high-fidelity off-road simulation toolbox, which incorporates the Hybrid Soft Soil Tire Model (HSSTM), a semi-empirical, experimentally validated off-road tire model, and the widespread simulation platform IPG CarMaker. This integration enables realistic simulations on deformable terrains and facilitates the development of off-road vehicle control algorithms for highly dynamic scenarios. The integrated vehicle-terrain model provides a tool for simulating the ride, handling, and mobility of off-road vehicles. HSSTM models the tire as discretized lumped masses with Kelvin-Voigt elements on a 3D deformable terrain. Unlike rigid-ring models, HSSTM captures tire flexibility, which is crucial for simulating tire dynamics on medium-hard to rigid terrains. To enhance computational efficiency, we adopt a Dynamic Terrain Adaptation (DTA) algorithm that models only a localized portion of the terrain around the vehicle, dynamically updating as the vehicle navigates. Simulations on deformable terrain in longitudinal and lateral scenarios (including force coupling) confirm a realistic representation of off-road dynamics based on the developed framework. The plug-in assesses contact patch pressure, normal and shear forces, elastic and plastic sinkage, and multi-pass effect.