Traditionally, terrain profilers have been evaluated based on their ability to reproduce measurements made from some
reference device (e.g., a rod and level). The measurement error inherent in these reference measurements has become
significant as terrain profilers have become more accurate. The fundamental technical challenge in the design of terrain
profilers is the removal of vehicle body motion from the height sensor measurement. The objective of this work is to
develop design criteria for an excitation event that will quantitatively highlight the abilities and inadequacies of terrain
profilers by testing the profilers under adverse measurement conditions. The design of a characteristic excitation event
must fulfill two requirements. First, the event should excite the terrain profiler chassis at its primary ride and wheel-hop
frequencies. Using these first two ride frequencies and the suspension damping ratio, relationships are developed that
relate these parameters to the geometric excitation event dimensions. The terrain profiler's test velocity is also
determined based on these frequencies. Second, the excitation event should be simple, light, inexpensive, and
reproducible to ensure that it is used. The result of this work is an excitation event that insures that the terrain profiler
will be excited to its highest attainable amplitude (near resonance). This excitation event provides the first step in
developing an accuracy test for modern terrain profilers.
As computational power builds to meet the needs of ground vehicle designers, the focus has begun to shift from
laboratory testing of prototype parts and subsystems to computational simulations of the vehicle. In the automotive and
defense industries, large strides have been made in simulating full vehicle responses, such as durability. These
simulations are most meaningful when excited by proper mathematical models that accurately characterize the terrain. It
is important to understand the roughness indices that are used to judge the terrain profiles. The state-of-the-art in terrain
characterization and modeling is reviewed in this work for models including Power Spectral Density (PSD), Markov
Chains, Autoregressive Integrated Moving Average (ARIMA), Parametric Road Spectrum (PRS), Shifted Spatial Range
Spectrum (SSR), Direct Spectrum Estimation (DSE) and Transformed Direct Spectrum Estimation (TrDSE). The
applicability, limitations, and benefits of these models are assessed based on their effectiveness in capturing the
stochastic nature of the terrain being characterized. A discussion of terrain characterization usage to advance reliability
testing concludes this work as an example of the applicability of this technology.
The principal excitation to a vehicle's chassis system is the road profile. Simulating a vehicle traversing long roads is
impractical and a method to produce short roads with given characteristics must be developed. There are many methods
currently available to characterize roads when they are assumed to be homogeneous. This work develops a method of
characterizing non-stationary road profile data using ARIMA (Autoregressive Integrated Moving Average) modeling
techniques. The first step is to consider the road to be a realization of an underlying stochastic process. Previous work
has demonstrated that an ARIMA model can be fit to non-stationary road profile data and the remaining residual
process is uncorrelated. This work continues the examination of the residual process of such an ARIMA model.
Statistical techniques are developed and used to examine the distribution of the residual process and the preliminary
results are demonstrated. The use of the ARIMA model parameters and residual distributions in classifying road
profiles is also discussed. By classifying various road profiles according to given model parameters, any synthetic road
realized from a given class of model parameters will represent all roads in that set, resulting in a timely and efficient
simulation of a vehicle traversing any given type of road.
During the vehicle design process, excitation loads are needed to correctly model the system response. The main source
of excitation to this dynamic system comes from the terrain. Characteristic models of terrain topology, therefore, would
allow for more accurate models and simulations of the system response. Terrain topology can be characterized as a
realization of an underlying stochastic process. It has been demonstrated that ARIMA modeling can be used to
characterize non-stationary road profiles. In this work it is suggested that ARIMA models of terrain topology can be
further developed by characterizing the previously deterministic autoregressive coefficients as random variables. In this
way uncertainty is introduced into the system parameters and propagated through the process to yield a distribution of
terrain topology. This distribution is then dependent on the distribution of the residuals as well as the distribution of the
ARIMA coefficients. The use of random variables to classify road types is discussed as possible future work.
Chassis loads and vehicle handling are primarily impacted by the road surface over which a vehicle is traversing. By
accurately measuring the geometries of road surfaces, one can generate computer models of these surfaces that will
allow more accurate predictions of the loads introduced to various vehicle components. However, the logistics and
computational power necessary to handle such large data files makes this problem a difficult one to resolve, especially
when vehicle design deadlines are impending. This work aims to improve this process by developing Markov Chain
models by which all relevant characteristics of road surface geometries will be represented in the model. This will
reduce the logistical difficulties that are presented when attempting to collect data and run a simulation using large data
sets of individual roads. Models will be generated primarily from measured road profiles of highways in the United
States. Any synthetic road realized from a particular model is representative of all profiles in the set from which the
model was derived. Realizations of any length can then be generated allowing efficient simulation and timely
information about chassis loads that can be used to make better informed design decisions, more quickly.
Load data representing severe customer usage is needed throughout a chassis development program; the majority of these chassis loads originate with the excitation from the road. These chassis loads are increasingly derived from vehicle simulations. Simulating a vehicle traversing long roads is simply impractical, however, and a greatly reduced set of characteristic roads must be found. In order to characterize a road, certain modeling assumptions must be made. Several models have been proposed making various assumptions about the properties that road profiles possess. The literature in this field is reviewed before focusing on two modeling assumptions of particular interest: the stationarity of the signal (homogeneity of the road) and the corresponding interval over which previous data points are correlated to the current data point. In this work, 2-D topographic road profiles are considered to be signals that are realizations of a stochastic process. The objective of this work is to investigate the stationarity assumption and the interval of influence for several carefully controlled sections of highway pavement in the United States. Two statistical techniques are used in analyzing these data: the autocorrelation and the partial autocorrelation. It is shown that the road profile signals in their original form are not stationary and have an extremely long interval of influence on the order of 25m. By differencing the data, however, it is often possible to generate stationary residuals and a very short interval of influence on the order of 250mm. By examining the autocorrelation and the partial autocorrelation, various versions of ARIMA models appear to be appropriate for further modeling. Implications to modeling the signals as Markov Chains are also discussed. In this way, roads can be characterized by the model architecture and the particular parameterization of the model. Any synthetic road realized from a particular model represents all profiles in this set. Realizations of any length can be generated, allowing efficient simulation and timely information about the chassis loads that can be used for design decisions. This work provides insights for future development in the modeling and characterization of 2-D topographic road profiles.