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Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13810))

Abstract

The issue of heavy vehicles rollover appears to be central in various sectors. This is due to the consequences entailed in terms of driver and passenger safety, other than considering aspects as environmental damaging and pollution. Therefore, several studies proposed estimative and predictive techniques to avoid this critical condition, with especially good results obtained by the using of Artificial Intelligence (AI) systems based on neural networks. Unfortunately, to conduct these kind of analyses a great quantity of data is required, with the same often difficult to be retrieved in sufficient numbers without incurring in unsustainable costs. To answer the problem, in this paper is presented a methodology based on synthetic data, generated in a specifically designed Matlab environment. This has been done by defining the characteristics of an heavy vehicle, a three axles truck, and making it complete maneuvers on surfaces and circuits purposely created to highlight rollover issues. After this phase, represented by the generation and processing of the data, follows the analysis of the same. This is the second major phase of the methodology, and contains the definition of a neural networks based algorithm. Referring to the nets, these are designed to obtain both the estimate and the prediction of four common rollover indexes, the roll angle and the Load Transfer Ratios (LTR, one for each axle). Very promising results were achieved in particular for the estimative part, offering new possibilities for the analysis of rollover issues both for the generation and the analysis of the data.

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Authors and Affiliations

  1. DIGEP - Politecnico di Torino, Turin, Italy

    Filippo Velardocchia & Guido Perboli

  2. DIMEAS - Politecnico di Torino, Turin, Italy

    Alessandro Vigliani

Authors
  1. Filippo Velardocchia
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  2. Guido Perboli
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  3. Alessandro Vigliani
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Corresponding author

Correspondence to Filippo Velardocchia .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Velardocchia, F., Perboli, G., Vigliani, A. (2023). Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-25599-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25598-4

  • Online ISBN: 978-3-031-25599-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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