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Using the Kalman Filter for Purposes of Road Condition Assessment

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Book cover Smart and Green Solutions for Transport Systems (TSTP 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1091))

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Abstract

This article provides a discussion on the use of the Kalman filter at the stage of analysis of vehicle motion dynamics to assess the condition of the road transport infrastructure. The tool used to record the vehicle motion dynamics was the Road Condition Tool application designed and implemented as a part of the international S-mile project under the programme entitled ERA-NET: Transport – Sustainable logistics and supply chains. An outcome of the data recording and analysis is a description of the operating condition of road pavements. The concept addressed in the study, namely the implementation of the Kalman filter at the data analysis stage, makes it possible to reduce the input data set, which affects the measuring tool’s response time. On the other hand, the adopted signal filtration procedure allows for the infrastructure condition to be described in a more favourable manner from the perspective of the conclusions to be formulated.

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Acknowledgements

The selected research presented in this paper has been financed from the means of the National Centre for Research and Development as a part of the international project within the scope of ERA-NET Transport III Programme “Smart platform to integrate different freight transport means, manage and foster first and last mile in supply chains (S-MILE)”.

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Authors and Affiliations

  1. Faculty of Transport, Silesian University of Technology, Katowice, Poland

    Marcin Staniek

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  1. Marcin Staniek
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Correspondence to Marcin Staniek .

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Editors and Affiliations

  1. Faculty of Transport, Silesian University of Technology, Katowice, Poland

    Grzegorz Sierpiński

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Staniek, M. (2020). Using the Kalman Filter for Purposes of Road Condition Assessment. In: Sierpiński, G. (eds) Smart and Green Solutions for Transport Systems. TSTP 2019. Advances in Intelligent Systems and Computing, vol 1091. Springer, Cham. https://doi.org/10.1007/978-3-030-35543-2_20

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