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

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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.

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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).

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Authors and Affiliations

  1. Department of Computer Sciences, University of Turin, Turin, Italy

    Marco Botta, Rossella Cancelliere, Leo Ghignone & Clara Luison

  2. Department E/E Systems, Centro Ricerche Fiat (CRF), Turin, Italy

    Fabio Tango

  3. Laboratory of Computer Sciences, LIP6, Sorbonne Université, Paris, France

    Patrick Gallinari

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  1. Marco Botta
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  2. Rossella Cancelliere
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Correspondence to Rossella Cancelliere.

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This work was supported by the EU Artemis Joint Undertaking research project HoliDes, Grant No. 332933. HoliDes addresses development and qualification of adaptive cooperative human–machine systems (AdCoS) where many humans and many machines act together, cooperatively, in a highly adaptive way to guarantee fluent and cooperative task achievement. http://www.holides.eu.

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Botta, M., Cancelliere, R., Ghignone, L. et al. Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training. Knowl Inf Syst 60, 1549–1564 (2019). https://doi.org/10.1007/s10115-019-01339-0

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