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An Electronic Stability Control algorithm has been developed for All-Terrain Vehicles. The algorithm is implemented on SEA’s Robotic Test Driver which has been customized to drive ATVs unmanned. The ESC algorithm is incorporated in the real-time controller. When activated, ESC monitors vehicle states and when they exceed pre-determined thresholds, ESC intervenes by dropping throttle and applying the vehicle’s brakes. The intention of this algorithm is to prevent yaw instability and ultimately limit the vehicle’s roll angle. ESC is implemented on two vehicles, which exhibit varying degrees of understeer. Test data is provided to illustrate the determination in setting threshold limits. The efficacy of the ESC is demonstrated by showing the system being enabled and disabled.

The use of platforms to carry vulnerable road user (VRU) targets has become increasingly necessary with the rise of automated driver assistance systems (ADAS) on vehicles. These ADAS features must be tested in a wide variety of collision-imminent scenarios which necessitates the use of strikable targets carried by an overrun-able platform. To enable the testing of ADAS sensors such as lidar, radar, and vision systems, S-E-A, a longtime supplier of vehicle testing equipment, has created the STRIDE robotic platform (Small Test Robot for Individuals in Dangerous Environments). This platform contains many of the key ingredients of other platforms on the market, such as a hot-swappable battery, E-stop, and mounting points for targets. However, the STRIDE platform additionally provides features which can enable non-routine testing such as: turning in place, driving with an app on a mobile phone, user-scripting, and steep grade climbing capability.

This paper presents tire data from an All-Terrain Vehicle (ATV). Data is collected on a Flat-Trac® machine where force and moment data were collected for three tires: one front tire and two rear tires. Tests were conducted at slip angles of +/-28 degrees, varying normal loads and inclinations angles. Additionally, tests were conducted at varying levels of inflation pressure. Key parametric data such as aligning moment, cornering and vertical stiffness levels are presented. One key finding from this study is that, in general, as inflation pressure increases cornering and aligning moment stiffness decreases. This effect is counter to typical passenger vehicle tires, where higher inflation pressure usually results in higher stiffness levels. Using results from the tire test data, understeer calculations are made for a vehicle under varying loading conditions.

A machine has been developed to measure the center-of-gravity (CG) location of a seat. This machine uses a system of pivots, a yaw bearing and two sensors to get the X, Y and Z CG of the seat. Test object mass is measured separately on a scale. A stable pendulum arrangement is used to get the CG location. Governing equations for the machine are shown in the paper and a typical test procedure is discussed. An error analysis is discussed and shows the requisite accuracy of platform angle, geometric dimensions, seat weight and applied weight in order to achieve the desired 3 mm accuracy target. A full system statistical analysis demonstrates that all X and Y CG locations, when compared with theoretical values, are off by less than 1 mm and well within the 3 mm accuracy target. For Z CG, the errors were shown to be 3.3 mm or lower with 95 percent confidence.

As part of their ongoing efforts to model and predict vehicle dynamics behavior, the US Army’s Ground Vehicle Systems Center procured a facility in two phases. The facility is called the Suspension Parameter Identification and Evaluation Rig (SPIdER) and has a capacity covering all of the military’s wheeled vehicles, with vehicle weights up to 100,000 lbs (45,400 kg), up to 150 inches wide, with any number of axles. The initial phase had the ability to measure bounce and roll kinematic and compliance properties. The SPIdER is the companion machine to the Vehicle Inertia Parameter Measuring Device (VIPER) which measures the inertia properties of vehicles of similar size. In 2015, the final phase of the SPIdER was completed. This phase includes ground plane wheel pad motion so that lateral, longitudinal, and aligning moment compliance and kinematic properties can be measured.

A machine has been developed to measure the complete inertia matrix; mass, center of gravity (CG) location, and all moments and products of inertia. Among other things these quantities are useful in studying engine vibrations, calculation of the torque roll axis, and in the placement of engine mounts. While the machine was developed primarily for engines it can be used for other objects of similar size and weight, and even smaller objects such as tires and wheels/rims. A key feature of the device is that the object, once placed on the test table, is never reoriented during the test cycle. This reduces the testing time to an hour or less, with the setup time being a few minutes to a few hours depending on the complexity of the shape of the object. Other inertia test methods can require up to five reorientations, separate CG measurement, and up to several days for a complete test.

This paper presents the development of a CarSim model of an All-Terrain Vehicle (ATV) that can be used to predict the handling and stability characteristic of the vehicle. The inertia and suspension characteristics of a subject ATV are measured and a model of the ATV is built in CarSim based on the measurements. A simplified suspension model is developed to convert the suspension compliance measurements into parameters suitable to a CarSim model. Procedures used to apply vehicle mass, inertia and suspension kinematics data in CarSim are also shown. The model is evaluated using predictions of vehicle response during a constant radius circle test. The simulation results of the maneuver are compared with the field test results shown in a recent CPSC report on ATV’s. Similar cornering characteristics are found in both results. Modifications are made to the model to study how changes to the ATV affect performance.

This paper presents a feedback linearization control technique as applied to a Roll Simulator. The purpose of the Roll Simulator is to reproduce in-field rollovers of ROVs and study occupant kinematics in a laboratory setting. For a system with known parameters, non-linear dynamics and trajectories, the feedback linearization algorithm cancels out the non-linearities such that the closed-loop dynamics behave in a linear fashion. The control inputs are computed values that are needed to attain certain desired motions. The computed values are a form of inverse dynamics or feed-forward calculation. With increasing system eigenvalue, the controller exhibits greater response time. This, however, puts a greater demand on the translational actuator. The controller also demonstrates that it is able to compensate for and reject a disturbance in force level.

This paper documents the vehicle response of a tractor-semitrailer following a sudden air loss (Blowout) in a steer axle tire while traveling at highway speeds. The study seeks to compare full-scale test data to predicted response from detailed heavy truck computer vehicle dynamics simulation models. Full-scale testing of a tractor-semitrailer experiencing a sudden failure of a steer axle tire was conducted. Vehicle handling parameters were recorded by on-board computers leading up to and immediately following the sudden air loss. Inertial parameters (roll, yaw, pitch, and accelerations) were measured and recorded for the tractor and semitrailer, along with lateral and longitudinal speeds. Steering wheel angle was also recorded. These data are presented and also compared to the results of computer simulation models. The first simulation model, SImulation MOdel Non-linear (SIMON), is a vehicle dynamic simulation model within the Human Vehicle Environment (HVE) software environment.

Torsional stiffness properties were developed for both a 53-foot box trailer and a 28-foot flatbed control trailer based on experimental measurements. In order to study the effect of torsional stiffness on the dynamics of a heavy truck vehicle dynamics computer model, static maneuvers were conducted comparing different torsional stiffness values to the original rigid vehicle model. Stiffness properties were first developed for a truck tractor model. It was found that the incorporation of a torsional stiffness model had only a minor effect on the overall tractor response for steady-state maneuvers up to 0.4 g lateral acceleration. The effect of torsional stiffness was also studied for the trailer portion of the existing model.

Validation was performed on an existing heavy truck vehicle dynamics computer model with roll stability control (RSC). The first stage in this validation was to compare the response of the simulated tractor to that of the experimental tractor. By looking at the steady-state gains of the tractor, adjustments were made to the model to more closely match the experimental results. These adjustments included suspension and steering compliances, as well as auxiliary roll moment modifications. Once the validation of the truck tractor was completed for the current configuration, the existing 53-foot box trailer model was added to the vehicle model. The next stage in experimental validation for the current tractor-trailer model was to incorporate suspension compliances and modify the auxiliary roll stiffness to more closely model the experimental response of the vehicle. The final validation stage was to implement some minor modifications to the existing RSC model.

A determination of the vehicle states and tire forces is critical to the stability of vehicle dynamic behavior and to designing automotive control systems. Researchers have studied estimation methods for the vehicle state vectors and tire forces. However, the accuracy of the estimation methods is closely related to the employed model. In this paper, tire lag dynamics is introduced in the model. Also application of estimation methods in order to improve the model accuracy is presented. The model is developed by using the global searching algorithm, a Genetic Algorithm, so that the model can be used in the nonlinear range. The extended Kalman filter and sliding mode observer theory are applied to estimate the vehicle state vectors and tire forces. The obtained results are compared with measurements and the outputs from the ADAMS full vehicle model. [15]