Estimating severe irregularities of road ahead based on preceding
vehicle responses
Abstract
[This paper is accepted by Elsevier journal Control Engineering
Practice.]
Road irregularities, e.g. potholes or bulges, will cause discomfort,
vehicle damages or even accidents, if not being carefully handled by
drivers. For driving safety and comfort, especially in highly automated
vehicles, there is a need for accurate and efficient way to estimate the
road condition ahead in advance. Existing direct sensing based methods
are difficult to give detailed irregularity information, while current
response based approaches require either too many measurements or
accurate system parameters to guarantee estimation performances.
Therefore, using the information of preceding vehicle responses, a
Kalman filter based algorithm to estimate severe road irregularities is
proposed. The single degree of freedom vertical dynamics model is
reorganized to reduce the measurement requirements of the Kalman filter.
To cope with the limited information of the preceding vehicle, the model
parameters are approximated according to suspension design theories.
Simulation and field data validation shows that the estimation algorithm
is effective and robust on different vehicles.