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


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.


Random projections Pseudo-inverse matrix Genetic algorithms Driver distraction recognition 



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.


  1. 1.
    Achlioptas D (2001) Database-friendly random projections. In: Proc. ACM Symp. on the principles of database systems, pp 274–281Google Scholar
  2. 2.
    Ajorloo H, Manzuri-Shalmani M T, Lakdashti A (2007) Restoration of damaged slices in images using matrix pseudo inversion. In: 22nd international symposium on computer and information sciencesGoogle Scholar
  3. 3.
    Alexander V, Annamalai P (2016) An elitist genetic algorithm based extreme learning machine. In: Senthilkumar M, Ramasamy V, Sheen S, Veeramani C, Bonato A, Batten L (eds) Computational intelligence, cyber security and computational models. Advances in intelligent systems and computing. Springer, SingaporeGoogle Scholar
  4. 4.
    Arriaga RI, Vempala S (1999) An algorithmic theory of learning: robust concepts and random projection. In: Proc. 40th annual symp. on foundations of computer science. IEEE Computer Society Press, pp 616–623Google Scholar
  5. 5.
    Badeva V, Morosov V (1991) Problemes incorrectements posès, thèorie et applications. Masson, ParisGoogle Scholar
  6. 6.
    Bayly M, Young KL, Regan MA (2009) Sources of distraction inside the vehicle and their effects on driving performance. In: Regan MA, Lee JD, Young KL (eds) Driver distraction: theory, effects and mitigation, 1st edn. CRC Press, Florida, USA, pp 191–213Google Scholar
  7. 7.
    Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proc. of the conference knowledge discovery and data mining KDD 2001, San Francisco CA, USAGoogle Scholar
  8. 8.
    Blaschke C, Breyer F, Freyer J, Limbacher R (2009) Driver distraction based lane-keeping assistance. Transp Res Part F Traffic Psychol Behav 12(4):288–299CrossRefGoogle Scholar
  9. 9.
    Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355MathSciNetzbMATHGoogle Scholar
  10. 10.
    Cancelliere R, Gosso A, Grosso A (2013) Neural networks for wind power generation forecasting: a case study. In: 10th IEEE international conference on networking, sensing and control (ICNSC), pp 666–671Google Scholar
  11. 11.
    Cancelliere R, Gai M, Gallinari P, Rubini L (2015) OCReP: an optimally conditioned regularization for pseudoinversion based neural training. Neural Netw 71:76–87CrossRefzbMATHGoogle Scholar
  12. 12.
    Cancelliere R, Deluca R, Gai M, Gallinari P, Rubini L (2017) An analysis of numerical issues in neural training based on pseudoinversion. Comput Appl Math 36:1–11MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Croo H, Bandmann M, Mackay G, Rumar, K, Vollenhoven P (2001) The role of driver fatigue in commercial road transport crashes. European Transport Safety CouncilGoogle Scholar
  14. 14.
    Dasgupta S, Gupta A (1999) An elementary proof of the Johnson-Lindenstrauss lemma. Technical Report TR-99-006, International Computer Science Institute, Berkeley, California, USAGoogle Scholar
  15. 15.
    Dingus TA, Klauer SG, Neale VL, Petersen A, Lee SE, Sudweeks J, Perez MA, Hankey J, Ramsey D, Gupta S, Bucher C, Doerzaph ZR, Jermeland J, Knipling RR (2006) The 100-car naturalistic driving study, phase II-results of the 100-car field experiment, Nat. Highway Traffic Safety Admin., Washington, DC, Dept. Transp., HS 810 593Google Scholar
  16. 16.
    Dong Y, Hu Z, Uchimura K, Murayama N (2011) Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans Intell Transp Syst 12(2):596–614CrossRefGoogle Scholar
  17. 17.
    Gallinari P, Cibas T (1999) Practical complexity control in multilayer perceptrons. Signal Process 74:29–46CrossRefzbMATHGoogle Scholar
  18. 18.
    Ghignone L, Cancelliere R (2016) Neural learning of heuristic functions for general game playing. In: 2nd International workshop on machine learning, optimization and big data, Lecture notes in computer science, vol 10122, SpringerGoogle Scholar
  19. 19.
    Golub G, Van Loan C (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, BaltimorezbMATHGoogle Scholar
  20. 20.
    Haigney D, Westerman SJ (2001) Mobile (cellular) phone use and driving: a critical review of research methodology. Ergonomics 44(2):132–143CrossRefGoogle Scholar
  21. 21.
    Halawa K (2011) A method to improve the performance of multilayer perceptron by utilizing various activation functions in the last hidden layer and the least squares method. Neural Process Lett 34:293–303CrossRefGoogle Scholar
  22. 22.
    Ham FM, Kostanic I (2001) Principles of neurocomputing for science & engineering. McGraw-Hill, Boston, MAGoogle Scholar
  23. 23.
    Haykin S (1999) Neural Networks, a comprehensive foundation. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  24. 24.
    Hecht-Nielsen R (1994) Context vectors: general purpose approximate meaning representations self-organized from raw data. In: Zurada JM, Marks RJ II, Robinson CJ (eds) Computational intelligence: imitating life. IEEE Press, Piscataway, pp 43–56Google Scholar
  25. 25.
    Hirayama T, Mase K, Miyajima C, Takeda K (2016) Classification of drivers neutral and cognitive distraction states based on peripheral vehicle behavior in drivers. IEEE Trans Intell Veh 1(2):148–157CrossRefGoogle Scholar
  26. 26.
    Hoel J, Jaffard M, Van Elslande P (2010) Attentional competition between tasks and its implications. In: European conference on human centred design for intelligent transport systems.
  27. 27.
    Hsieh L, Young R, Seaman S (2012) Development of the enhanced peripheral detection task: a surrogate test for driver distraction. SAE Int J Passeng Cars Electronic Electr Syst 5(1):317–325CrossRefGoogle Scholar
  28. 28.
    Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48CrossRefzbMATHGoogle Scholar
  29. 29.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529CrossRefGoogle Scholar
  30. 30.
    Igelnik B, Pao YH, LeClair SR, Shen CY (1999) The ensemble approach to neural-network learning and generalization. IEEE Trans Neural Netw 10(1):19–30CrossRefGoogle Scholar
  31. 31.
    Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proc. 30th symp. on theory of computing. ACM, pp 604–613Google Scholar
  32. 32.
    Johnson WB, Lindenstrauss J (1984) Extensions of Lipshitz mapping into Hilbert space. Contemp Math 26:189–206CrossRefzbMATHGoogle Scholar
  33. 33.
    Klauer S G, Dingus T A, Neale V L, Sudweeks J D, Ramsey D J (2006) The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data, Nat. Highway Traffic Safety Admin. (NHTSA), Washington, DC, USA, Tech. Rep. DOT HS 810 594Google Scholar
  34. 34.
    Kohno K, Kawamoto M, Inouye Y (2010) A matrix pseudoinversion lemma and its application to block-based adaptive blind deconvolution for MIMO systems. IEEE Trans Circuits Syst 57:1449–1462MathSciNetCrossRefGoogle Scholar
  35. 35.
    Lee JD, Young KL, Regan MA (2008) Defining driver distraction. In: Regan MA, Lee JD, Young KL (eds) Driver distraction: theory, effects, and mitigation. CRC Press Taylor & Francis Group, Boca Raton, pp 31–40CrossRefGoogle Scholar
  36. 36.
    Liang Y, Lee JD (2010) Combining cognitive and visual distraction: less than the sum of its parts. Accid Anal Prev 42(3):881–890CrossRefGoogle Scholar
  37. 37.
    Liu H, Jiao B, Peng L, Zhang T (2015) Extreme learning machine based on improved genetic algorithm. In: 5th international conference on information engineering for mechanics and materials (ICIMM)Google Scholar
  38. 38.
    Lowe D (1989) Adaptive radial basis function nonlinearities, and the problem of generalisation. In: Proc. 1st Inst. Electr. Eng. Int. Conf. Artif. Neural Netw., pp 171–175Google Scholar
  39. 39.
    Merat N, Jamson A H (2007) Multisensory signal detection: a tool for assessing driver workload during IVIS management. In: Proceedings of the 4th international symposium on human factors in driver assessment, training and vehicle designGoogle Scholar
  40. 40.
    Merat N, Johansson E, Engstrom J, Chin E, Nathan F, Victor T (2007) Specification of a secondary task to be used in safety assessment of IVIS. Adaptive integrated driver-vehicle interfaceGoogle Scholar
  41. 41.
    McKnight AJ, McKnight AS (1993) The effect of cellular phone use upon driver attention. Accid Anal Prev 25(3):259–265CrossRefGoogle Scholar
  42. 42.
    Nguyen TD, Pham HTB, Dang VH (2010) An efficient Pseudo Inverse matrix-based solution for secure auditing. In: IEEE international conference on computing and communication technologies, research, innovation, and vision for the futureGoogle Scholar
  43. 43.
    Pao YH, Takefuji Y (1992) Functional-link net computing, theory, system architecture, and functionalities. IEEE Comput 25(5):76–79CrossRefGoogle Scholar
  44. 44.
    Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of random vector functional-link net. Neurocomputing 6:163–180CrossRefGoogle Scholar
  45. 45.
    Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley, Reading, MAzbMATHGoogle Scholar
  46. 46.
    Park J, Sandberg IW (1991) Universal approximation using radialbasis-function networks. Neural Comput 3:246–257CrossRefGoogle Scholar
  47. 47.
    Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242CrossRefGoogle Scholar
  48. 48.
    Qiao L, Sato M, Takeda H (1995) Learning algorithm of environmental recognition in driving vehicle. IEEE Trans Syst Man Cybern 25(6):917–925CrossRefGoogle Scholar
  49. 49.
    Ranney TA, Garrott WR, Goodman MJ (2001) NHTSA driver distraction research: past, present, and future, National Highway Traffic Safety Administration, pp 1–8Google Scholar
  50. 50.
    Regan MA, Hallet C, Gordon CP (2011) Driver distraction and driver inattention: definition, relationship and taxonomy. Accid Anal Prev J 43:1771–1781CrossRefGoogle Scholar
  51. 51.
    Rumelhart DE, Hinton GE, Williams RJ (1996) Learning internal representations by error propagation. Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362Google Scholar
  52. 52.
    Rupp GL (2010) Performance metrics for assessing driver distraction: the quest for improved road safety. SAE International, WarrendaleCrossRefGoogle Scholar
  53. 53.
    Sharma R, Bist AS (2015) Genetic algorithm based weighted extreme learning machine for binary imbalance learning. In: International conference on cognitive computing and information processing (CCIP)Google Scholar
  54. 54.
    Tango F, Botta M (2009) Evaluation of distraction in a driver-vehicle-environment framework: an application of different data-mining techniques. In: Proc. 9th industrial conference on data mining (ICDM09), Springer, Leipzig, GermanyGoogle Scholar
  55. 55.
    Tango, F, Minin L, Montanari, R, Botta, M (2010). Non-intrusive detection of driver distraction using machine learning algorithms. In: the proceeding of the XIX European conference on artificial intelligence (ECAI). IOS Press Amsterdam, The NetherlandsGoogle Scholar
  56. 56.
    Tiago M, Rui A, Carlos Henggeler A, Dulce G (2013) Genetically optimized extreme learning machine. In: IEEE 18th conference on emerging technologies & factory automation (ETFA)Google Scholar
  57. 57.
    Tikhonov AN (1963) Solution of incorrectly formulated problems and the regularization method. Soviet Math 4:1035–1038zbMATHGoogle Scholar
  58. 58.
    Tikhonov AN, Arsenin VY (1977) Solutions of ill-posed problems. Winston, WashingtonzbMATHGoogle Scholar
  59. 59.
    Vempala S (1998) Random projection: a new approach to VLSI layout. In: Proc. 39th annual symp. on foundations of computer science. IEEE Computer Society PressGoogle Scholar
  60. 60.
    Wickens CD (2002) Multiple Resources and performance prediction. Theor Issues Ergon Sci 3(2):159–177CrossRefGoogle Scholar
  61. 61.
    Woeller M, Blaschke C, Schhindl T, Schuller B, Faerber B, Mayer S, Trefflich B (2011) Online driver distraction detection using long short-term memory. IEEE Trans Intell Transp Syst 12(2):574–582CrossRefGoogle Scholar
  62. 62.
    Xiang C, Ding SQ, Lee TH (2005) Geometrical interpretation and architecture selection of MLP. IEEE Trans Neural Netw 16(1):84–96CrossRefGoogle Scholar
  63. 63.
    Young K, Regan M (2007) Driver distraction: a review of the literature. Distracted driving. Australian College Road Safety, Sydney, pp 379–405Google Scholar
  64. 64.
    Yu D, Deng L (2012) Efficient and effective algorithms for training single-hidden-layer neural networks. Pattern Recogniti Lett 33:554–558CrossRefGoogle Scholar
  65. 65.
    Zhang H, Smith MRH, Witt GJ (2006) Identification of real-time diagnostic measures of visual distraction with an automatic eye-tracking system. Hum Factors 48(4):805–821CrossRefGoogle Scholar

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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