Developing Markov chain models for road surface simulation (original) (raw)

SPIE Proceedings, 2007

Abstract

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.

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