Accurate terrain models provide the chassis designer with a powerful tool to make informed design decisions early in the design process. It is beneficial to characterize the terrain as a stochastic process, allowing limitless amounts of synthetic terrain to be created from a small number of parameters. A continuous-state Markov chain is proposed as an alternative to the traditional discrete-state chain currently used in terrain modeling practice. For discrete-state chains, the profile transitions are quantized then characterized by a transition matrix (with many values). In contrast, the transition function of a continuous-state chain represents the probability density of transitioning between any two states in the continuum of terrain heights. The transition function developed in this work uses a location-scale distribution with polynomials modeling the parameters as functions of the current state. A maximum likelihood estimator is used to parameterize the coefficients of these polynomials. The logistic distribution is applied as a proof of concept, and other distributions are discussed as targets of future research.


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    Title :

    Developing a Compact Continuous-State Markov Chain for Terrain Road Profiles


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    SAE 2013 World Congress & Exhibition ; 2013



    Publication date :

    2013-04-08




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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