A human-in-the-loop probabilistic CNN-fuzzy logic framework for accident prediction in vehicular networks

Journal article


Usman, Muhammad, Carie, Anil, Marapelli, Bhaskar, Bedru, Hayat Dino and Biswas, Kamanashis. (2021). A human-in-the-loop probabilistic CNN-fuzzy logic framework for accident prediction in vehicular networks. IEEE Sensors Journal. 21(14), pp. 15496-15503. https://doi.org/10.1109/JSEN.2020.3023661
AuthorsUsman, Muhammad, Carie, Anil, Marapelli, Bhaskar, Bedru, Hayat Dino and Biswas, Kamanashis
Abstract

The vehicle accident prediction methods are designed to improve the vehicular safety and reduce the rescue response time in the case of an accident. The existing accident prediction methods, however, do not involve Human-in-the-Loop, i.e., do not consider the emotional state of a driver to predict the likelihood of an accident. We propose a Probabilistic Convolutional Neural Network (CNN)-Fuzzy Logic framework that involves Human-in-the-Loop and takes into account the multiple input streams of sensor generated data, i.e., human emotions and traffic data. The features extracted from the CNN model are fed to our designed probabilistic graph-based inference model to determine the accident probability. The probability is then mapped with accident severity through fuzzy membership functions for accident prediction. The experiment results show the promising performance of our proposed framework, i.e., 93.1% accuracy of face expressions, 76.2% accuracy of heartbeat, and 76.9% accuracy of traffic inputs and predicts the accident likelihood with 90% accuracy. The comparison, with related works, shows that the proposed framework can predict accidents with higher probabilities.

Keywordsaccident prediction; convolution neural network; deep learning; fuzzy logic; human-in-the-loop
Year2021
JournalIEEE Sensors Journal
Journal citation21 (14), pp. 15496-15503
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN1530-437X
Digital Object Identifier (DOI)https://doi.org/10.1109/JSEN.2020.3023661
Scopus EID2-s2.0-85110598970
Research or scholarlyResearch
Page range15496-15503
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online11 Sep 2020
Publication process dates
Accepted03 Sep 2020
Deposited04 Jul 2022
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