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The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

The paper describes a methodology to co-simulate, with high fidelity, simultaneously and in one computational framework, all of the main vehicle subsystems for improved engineering design. The co-simulation based approach integrates in MATLAB/Simulink a physics-based tire model with high fidelity vehicle dynamics model and an accurate powertrain model allowing insights into 1) how the dynamics of a vehicle affect fuel consumption, quality of emission and vehicle control strategies and 2) how the choice of powertrain systems influence the dynamics of the vehicle; for instance how the variations in drive shaft torque affects vehicle handling, the maximum achievable acceleration of the vehicle, etc. The goal of developing this co-simulation framework is to capture the interaction between powertrain and rest of the vehicle in order to better predict, through simulation, the overall dynamics of the vehicle.

In this paper we present a project that interfaced the National Advanced Driving Simulator (NADS) with SynChrono, a module of the Project Chrono open source simulation platform, to enable real-time, physics-based simulation of multiple autonomous vehicles (AVs) interacting with manned vehicles. In this setup, a driver at NADS, at the University of Iowa, participates in a traffic scenario that involves AVs that run at the University of Wisconsin-Madison on a cluster supercomputer. The NADS simulator is a driving simulator giving the “most realistic driving simulation experience in the country” [1]. Thanks to its actuators, it can move across its 64-foot by 64-foot bay, rotate and tilt, to emulate vehicle movement and vibrations. In addition, the human driver drives in a full-size cab, surrounded by LED monitors, resulting in an immersive, high fidelity driving simulation experience.

1 This paper describes a modeling, simulation, and visualization framework aimed at enabling physics-based analysis of ground vehicle mobility. This framework, called Chrono, has been built to leverage parallel computing both on distributed and shared memory architectures. Chrono is both modular and extensible. Modularity stems from the design decision to build vertical applications whose goal is to reduce the end-to-end time from vision-to-model-to-solution-to-visualization for a targeted application field. The extensibility is a consequence of the design of the foundation modules, which can be enhanced with new features that benefit all the vertical applications. Two factors motivated the development of Chrono. First, there is a manifest need of modeling approaches and simulation tools to support mobility analysis on deformable terrain.

In the context of off-road vehicle simulations, deformable terrain models mostly fall into three categories: simple visualization of the deformed terrain only, use of empirical relationships for the deformation, or finite/discrete element approaches for the terrain. A real-time vehicle dynamics simulation with a physics-based tire model (brush, ring or beam-based models) requires a terrain model that accurately reflects the deformation and response of the soil to all possible inputs of the tire in order to correctly simulate the response of the vehicle. The real-time requirement makes complex finite/discrete element approaches unfeasible, and the use of a ring or beam -based tire model excludes purely empirical terrain models. We present the development of a three-dimensional vehicle/terrain interaction model which is comprised of a tire and deformable terrain model to be used with a real-time vehicle dynamics simulator.

The U.S. Army Tank Automotive Research Development and Engineering Center (TARDEC) and the U.S. Army Corps of Engineers (USACE) are improving modeling and simulation technologies in order to predict the performance of Army ground platforms with a high degree of confidence. In order to provide a framework within which to evaluate the simulation technologies and provide a measure of the progress of the effort, a suite of virtual test operating procedures are being implemented. This framework is called the Virtual Evaluation Suite (VES). It is applicable to the study of ground vehicle stability, handling, ride, mobility, and durability over all terrains under all weather conditions. Although developed in order to evaluate simulation technologies, the VES may be considered a simulation that could be used to exercise any ground platform model that meets the VES standard vehicle interface.

A virtual, all-season test site for use in real-time vehicle simulators and mobility models was constructed of an Army firing range in Northern Vermont. The virtual terrain will mimic the terrain of our Virtual Data Acquisition and Test Site (VDATS) at Ethan Allen Firing Range (EAFR). The objective is to realistically simulate on- and off-road vehicle performance in all weather conditions for training and vehicle design for the US Army. To this end, several spatial datasets were needed to accurately map the terrain and estimate the state-of-the-ground and terrain strength at different times of the year. The terrain strength is characterized by terramechanics properties used in algorithms to calculate the forces at the vehicle-terrain interface. The performance of the real vehicles will be compared to the simulated vehicle performance of operator-in-the-loop and unmanned vehicles for validation of the simulations.

This paper discusses an approach to construct a high fidelity surrogate tire model using a two-phase optimization-based algorithm that draws on data generated by off-line nonlinear ABAQUS tire simulations. It subsequently describes the process of Simulink-based interfacing of the resulting surrogate model to a full ADAMS vehicle model to enable accurate and expeditious durability studies. The two-phase surrogate model construction relies on an identification method that draws on the Instantaneous Center Manifold (ICM) theory. In the proposed method, a generally forced non-autonomous nonlinear structural system is represented as a sequence of harmonically excited autonomous nonlinear systems. The close-form solution of each of these systems is produced using the ICM theory. The first phase of the surrogate model construction uses an optimal Orthogonal Matching Pursuit (OMP) algorithm to unify all ICMs used to approximate the reaction force of the tire at its spindle.

This paper addresses some aspects of an on-going multiyear research project for US Army TARDEC. The focus of the research project has been the enhancement of the overall vehicle reliability prediction process. This paper describes briefly few selected aspects of the new integrated reliability prediction approach. The integrated approach uses both computational mechanics predictions and experimental test databases for assessing vehicle system reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level.

The U.S. Army uses the root mean square and power spectral density of elevation to characterize road/terrain (off-road) roughness for durability. This paper describes research aimed toward improving these metrics. The focus is on taking previously developed metrics and applying them to mathematically generated terrains to determine how each metric discerns the relative roughness of the terrains from a vehicle durability perspective. Multiple terrains for each roughness level were evaluated to determine the variability for each terrain rating metric. One method currently under consideration is running a relatively simple, yet vehicle class specific, model over a given terrain and using predicted vehicle response(s) to classify or characterize the terrain.

We present an approach in which an open-source software infrastructure is used for testing the behavior of autonomous vehicles through computer simulation. This software infrastructure is called CAVE, from Connected Autonomous Vehicle Emulator. As a software platform that allows rapid, low-cost and risk-free testing of novel designs, methods and software components, CAVE accelerates and democratizes research and development activities in the field of autonomous navigation.

This paper discusses the development of a novel deformable terrain database and its use in a co-simulation environment with a multibody dynamics vehicle model. The implementation of the model includes a general tire-terrain traction model which is modular to allow for any type of tire model that supports the Standard Tire Interface[1] to operate on the terrain. This allows arbitrarily complex tire geometry to be used, which typically has a large impact on the mobility performance of vehicles operating on deformable terrains. However, this gain in generality comes at the cost that popular analytical pressure-sinkage terramechanics models cannot be used to find the normal pressure and shear stress of the contact patch. Pressure and shear stress are approximated by combining the contributions from tire normal forces, shear stresses and bulldozing forces due to soil rutting.

This study outlines an approach for speeding up the simulation of the dynamic response of vehicle models that include hysteretic nonlinear tire components. The method proposed replaces the hysteretic nonlinear tire model with a surrogate model that emulates the dynamic response of the actual tire. The approach is demonstrated via a dynamic simulation of a quarter vehicle model. In the proposed methodology, training information generated with a reduced number of harmonic excitations is used to construct the tire hysteretic force emulator using a Neural Network (NN) element. The proposed approach has two stages: a learning stage, followed by an embedding of the learned model into the quarter car model. The learning related main challenge stems from the attempt to capture with the NN element the behavior of a hysteretic element whose response depends on its loading history.

Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high.

Accurate terrain models provide the chassis designer with a powerful tool to make informed design decisions early in the design process. It is beneficial to characterize the terrain as a stochastic process, allowing limitless amounts of synthetic terrain to be created from a small number of parameters. A continuous-state Markov chain is proposed as an alternative to the traditional discrete-state chain currently used in terrain modeling practice. For discrete-state chains, the profile transitions are quantized then characterized by a transition matrix (with many values). In contrast, the transition function of a continuous-state chain represents the probability density of transitioning between any two states in the continuum of terrain heights. The transition function developed in this work uses a location-scale distribution with polynomials modeling the parameters as functions of the current state.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

Laboratory based durability simulation has become an increasingly important component of vehicle system design validation and production release. It offers several advantages over field testing which has driven its adoption in the automotive and military sectors. Among these advantages are 1) repeatability, 2) earlier testing, 3) isolation of subsystems or components and 4) ability to compress and/or accelerate the testing. In this paper we present time-domain methods and techniques adapted, implemented and used at TARDEC to reduce the time required to perform a laboratory durability test of a full vehicle system, subsystem or component. Specifically, these methods approach a durability schedule holistically by considering all events/surfaces, repeats and channels of interest. They employ the standard Generic Stress Life (GSL) approach, utilizing rain flow cycle counting and a minimum-average method of identifying segments of the events which are less severe.

The purpose of this study is to improve predicted tire forces for vehicle simulations on off-road terrain and for simulations incorporating terrain features such as curbs, pavement markers or potholes. The model presented in this paper describes the longitudinal behavior of the tire for traversing high-fidelity terrain profiles. An extended rolling radial-interradial tire model is used to estimate the pressure distribution of the tire contact patch, while a tangential spring model of the tire carcass is used to estimate tractive forces at the tire/road interface. Due to the complexity of the model real-time simulation is not possible, however it is useful for off-line simulations incorporating rough terrain or short-wavelength terrain features.

An exact approach to linearize the equations of motion of multibody systems is presented. The method has general applicability and it is well suited to linearize the index-3 Differential Algebraic Equations (DAE) governing the state of a dynamical system. Moreover, the method was extended to linearize a dynamical system in terms of user-defined coordinates without the need to reformulate the governing equations; this feature is of particular interest in disciplines like rotordynamics where eigensolutions are requested in terms of coordinates defined in a rotating frame. Contrary to other linearization methods, the proposed approach implements a closed-form computation of the linearized equations of motion; all second order effects are taken into account and no numerical differentiation is required. The proposed method inflates the governing equations and then computes a set of sensitivities that provide the linearization of interest.

The U.S. Army uses the root mean square and power spectral density of elevation to characterize road/terrain (off-road) roughness for durability. This paper describes research aimed toward improving these metrics. The focus is on taking previously developed metrics and applying them to mathematically generated terrains to determine how each metric discerns the relative roughness of the terrains from a vehicle durability perspective. Multiple terrains for each roughness level were evaluated to determine the variability for each terrain rating metric. One method currently under consideration is running a relatively simple, yet vehicle class specific, model over a given terrain and using predicted vehicle response(s) to classify or characterize the terrain.