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Deep CNN-based autonomous system for safety measures in logistics transportation

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A Correction to this article was published on 02 July 2021

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Abstract

The careless activity of drivers in logistics transportation is a primary reason inside the vehicle during road accidents. This research aims to reduce the number of accidents caused by a failure of the driver in logistics transportation by incorporating an autonomous system. We propose a convolutional neural network -based architecture to recognize and classify different positions which cause road accidents. The proposed system is evaluated with the State Farm Distracted Driver Database, which included examples illustrating ten different driving positions like reaching behind and talking to the passenger, making up, safe driving, talking on the phone, clothing, checking right/left hand, right/left hand, and running the radio. The proposed approach has also been tested against recent algorithms and evaluated. Our model has obtained 98.98% accuracy compared to other types of approaches with different descriptors and classification techniques

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Funding

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Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences, Ferhat Abbas University Setif, Setif, Algeria

    Abdelkarim Rouari & Abdelouahab Moussaoui

  2. Computer Science Department, University of Caen, Caen, France

    Youssef Chahir

  3. Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom

    Hafiz Tayyab Rauf

  4. Faculty of applied computing and technology, Noroff University College, Kristiansand, Norway

    Seifedine Kadry

Authors
  1. Abdelkarim Rouari
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  2. Abdelouahab Moussaoui
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  3. Youssef Chahir
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  4. Hafiz Tayyab Rauf
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  5. Seifedine Kadry
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [AR, AM, YC, HTR, and SK]. The first draft of the manuscript was written by [AR], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hafiz Tayyab Rauf.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by Vicente Garcia Diaz.

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The original article has been updated: Due to corresponding author name update.

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Rouari, A., Moussaoui, A., Chahir, Y. et al. Deep CNN-based autonomous system for safety measures in logistics transportation. Soft Comput 25, 12357–12370 (2021). https://doi.org/10.1007/s00500-021-05949-1

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