  The extent of vibrations experienced by a vehicle driving over natural
terrain defines its ride quality. Generally, surface irregularities, ranging
from single discontinuities to random variations of the elevation profile, act
as a major source of excitation that induces vibrations in the vehicle body
through the tire-soil interaction and suspension system. Therefore, the ride
response of off-road vehicles is tightly connected with the ground properties.
The objective of this research is to develop a model-based observer that
estimates automatically terrain parameters using available onboard sensors. Two
acceleration signals, one coming from the vehicle body and one from the wheel
suspension, are fed into a dynamic vehicle model that takes into account
tire/terrain interaction to estimate ground properties. To solve the resulting
nonlinear simultaneous state and parameter estimation problem, the cubature
Kalman filter is used, which is shown to outperform the standard extended
Kalman filter in terms of accuracy and stability. An extensive set of
simulation tests is presented to assess the performance of the proposed
estimator under various surface roughness and deformability conditions. Results
show the potential of the proposed observer to estimate automatically terrain
properties during operations that could be implemented onboard of a general
family of intelligent vehicles, ranging from off-road high-speed passenger cars
to lightweight and low-speed planetary rovers.
