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Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

This paper describes a human-in-the-loop motion-based simulator which was built to perform controlled fuel economy measurements for both a conventional and hybrid electric HMMWV. The simulator was constructed with a driver's console, visualization system, and audio system all of which were mounted on the motion base simulator. These interface devices were then integrated with a real-time dynamics model of the HMMWV. The HMMWV dynamics model was built using the real-time vehicle modeling tool SimCreator®, which, in turn was integrated with two powertrain models implemented with Gamma Technologies GT-Drive product. These two powertrains consisted of a conventional configuration and a series hybrid-electric configuration. They were then run on four different standard Army fuel consumption courses to replicate tests which had previously been conducted at the proving ground. Experiments were performed for varying speeds with two experienced proving ground drivers.

As part of the evaluation of vehicle simulation models, a vehicle dynamics engineer typically desires to compare simulation results to test data from actual vehicles and/or results from known, or higher fidelity simulations. Depending on the type of model, several types of tests and/or maneuvers may need to be compared. For military vehicles, there is the additional requirement to run specific types of maneuvers for vehicle model evaluations to ensure that the vehicle complies with procurement requirements. A thorough evaluation will run two different categories of tests/maneuvers. The first category consists of laboratory type tests that include weight distribution, kinematics and compliance, steering ratio, and other static measures. The second category consists of dynamic maneuvers that include handling, drive train, braking, ride, and obstacle types. In this paper, a process for proper evaluation of vehicle simulation models is presented.

The United States Army Research, Development, and Engineering Command’s Tank Automotive Research, Development and Engineering Center (U.S. Army RDECOM-TARDEC) laboratories is seeking to advance modeling and simulation methods used for predicting the performance of ground vehicles. TARDEC typically generates non-real-time models of its vehicles using DADS [1]: a general purpose commercial, multi-body software package based on a Cartesian coordinate formulation. TARDEC also currently uses SimCreator [2], [3] to develop real-time multi-body vehicle models. SimCreator uses recursive techniques to perform the simulations in real-time. The goal of the study presented here was to develop rapid conversion methods for translating models of DADS and other commercial multi-body software packages into SimCreator models. A procedure that can be automated was developed to convert a DADS model of a High-Mobility Multipurpose Wheeled Vehicle (HMMVW) to a SimCreator model.

Standard visual database modeling practices in driving simulation reduce geometric complexity of terrain surfaces by using texture maps to simulate high frequency detail. Typically the vehicle dynamics model queries a correlated database that contains the polygons from the high level of detail of the visual database. However the vehicle dynamics database does not contain any of the high frequency information included in the texture maps. To overcome this issue and enhance both the visual and vehicle dynamics databases, a mathematical model of the high frequency content of the ground surface is developed using a set of Non-Uniform Rational B-Splines (NURBS) patches. The patches are combined in the terrain query by superimposing them over the low-frequency polygonal terrain, reintroducing the missing content. The patches are also used to generate Bump Map textures for the image generator so that the visual representation matches the terrain query.

The United States Research, Development, and Engineering Command's Tank Automotive Research, Development and Engineering Center (U.S. Army RDECOM-TARDEC) laboratories, in accordance with a Science and Technology Objective (STO), are looking for both real-time and non real-time modeling and simulation methods to advance the capabilities and methodologies used in the Army's Modeling and Simulation areas. Advancing technologies require TARDEC to model new components and vehicles that may be significantly different from prior systems. TARDEC's ultimate goal is to develop the capability to model and accurately recreate the behaviors of advance technologies that may present themselves in the Army's Transformation and its Future Combat System (FCS) of vehicles in real-time with the soldier-in-the-loop. This paper discusses TARDEC's effort to accomplish this goal.

Simulations of ground vehicles are extensively used by military and commercial vehicle developers to aid in the design process. In the past, ground vehicle simulations have focused on non-real-time models. However with the advancement of computers and modeling methodologies, real-time multi-body models have become one of the standard tools used by vehicle developers. Multi-body models are composed of joint, body, and force elements which map well into a modular modeling approach. Based on recursive techniques a set of reusable components were developed for use in a graphical simulation and modeling environment. The components were then connected to form a real-time multi-body model of a Ford Taurus. Finally, the Taurus model was integrated with simulator cueing subsystems to build a complete driving simulator. The performance of the Taurus model was compared with test data. It was found that the vehicle model was both accurate and ran much faster than real-time.

The use of driving simulation in vehicle design and development is growing. For maximal benefit, vehicle models used in the driving simulator must be rapidly reconfigurable and easy to develop. To evaluate potential modeling concepts, vehicle dynamics and vehicle subsystems are developed using modular model components. These are integrated with simulator cueing subsystems, using the same modeling concepts, to build a complete driving simulator. It was found that the vehicle dynamics and the simulator could be reconfigured easily to meet user needs.