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Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training

  • Marco Botta
  • Rossella CancelliereEmail author
  • Leo Ghignone
  • Fabio Tango
  • Patrick Gallinari
  • Clara Luison
Regular Paper

Abstract

There is an accumulating evidence that distracted driving is a leading cause of vehicle crashes and accidents. In order to support safe driving, numerous methods of detecting distraction have been proposed, which are empirically focused on certain driving contexts and gaze behaviour. This paper aims at illustrating a method for the non-intrusive and real-time detection of visual distraction based on vehicle dynamics data and environmental data, without using eye-tracker information. Experiments are carried out in the context of the automotive domain of the European project Holides, which addresses development and qualification of adaptive cooperative human–machine systems, and is co-funded by ARTEMIS Joint Undertaking and Italian University, Educational and Research Department. The collected data are analysed by a single-layer feedforward neural network trained through pseudo-inversion methods, characterized by direct determination of output weights given randomly set input weights and biases. One main feature of our work is the convenient setting of input weights by the so-called sparse random projections: the presence of a great number of null elements in the involved matrices makes especially parsimonious the use at run time of the trained network. Moreover, we use a genetic approach to better explore the input weights network space. The obtained results show better performance with respect to classical pseudo-inversion methods and effective and parsimonious use of memory resources.

Keywords

Random projections Pseudo-inverse matrix Genetic algorithms Driver distraction recognition 

Notes

Acknowledgements

The activity has been partially carried on in the context of the Visiting Professor Program of the Gruppo Nazionale per il Calcolo Scientifico (GNCS) of the Italian Istituto Nazionale di Alta Matematica (INdAM).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of TurinTurinItaly
  2. 2.Department E/E SystemsCentro Ricerche Fiat (CRF)TurinItaly
  3. 3.Laboratory of Computer Sciences, LIP6Sorbonne UniversitéParisFrance

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