Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Knowledge and Information Systems
Published in: Knowledge and Information Systems 3/2019

14-02-2019 | Regular Paper

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

Authors: Marco Botta, Rossella Cancelliere, Leo Ghignone, Fabio Tango, Patrick Gallinari, Clara Luison

Published in: Knowledge and Information Systems | Issue 3/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article PRS: efficient range skyline computation on massive data via presorting
next article A trust-based detection scheme to explore anomaly prevention in social networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
1
0 = Not pressed; 1 = Pressed.
 
Literature
1.
Achlioptas D (2001) Database-friendly random projections. In: Proc. ACM Symp. on the principles of database systems, pp 274–281
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 sciences
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, Singapore
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 616–623
5.
Badeva V, Morosov V (1991) Problemes incorrectements posès, thèorie et applications. Masson, Paris
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 191–213
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, USA
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):288–299CrossRef
9.
Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355MathSciNetMATH
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 666–671
11.
Cancelliere R, Gai M, Gallinari P, Rubini L (2015) OCReP: an optimally conditioned regularization for pseudoinversion based neural training. Neural Netw 71:76–87CrossRefMATH
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 36:1–11MathSciNetCrossRefMATH
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 Council
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, USA
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 810 593
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):596–614CrossRef
17.
Gallinari P, Cibas T (1999) Practical complexity control in multilayer perceptrons. Signal Process 74:29–46CrossRefMATH
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, Springer
19.
Golub G, Van Loan C (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, BaltimoreMATH
20.
Haigney D, Westerman SJ (2001) Mobile (cellular) phone use and driving: a critical review of research methodology. Ergonomics 44(2):132–143CrossRef
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 34:293–303CrossRef
22.
Ham FM, Kostanic I (2001) Principles of neurocomputing for science & engineering. McGraw-Hill, Boston, MA
23.
Haykin S (1999) Neural Networks, a comprehensive foundation. Prentice Hall, Upper Saddle RiverMATH
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 43–56
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):148–157CrossRef
26.
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):317–325CrossRef
28.
Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48CrossRefMATH
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):513–529CrossRef
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):19–30CrossRef
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 604–613
32.
Johnson WB, Lindenstrauss J (1984) Extensions of Lipshitz mapping into Hilbert space. Contemp Math 26:189–206CrossRefMATH
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 810 594
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 57:1449–1462MathSciNetCrossRef
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 31–40CrossRef
36.
Liang Y, Lee JD (2010) Combining cognitive and visual distraction: less than the sum of its parts. Accid Anal Prev 42(3):881–890CrossRef
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)
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 171–175
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 design
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 interface
41.
McKnight AJ, McKnight AS (1993) The effect of cellular phone use upon driver attention. Accid Anal Prev 25(3):259–265CrossRef
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 future
43.
Pao YH, Takefuji Y (1992) Functional-link net computing, theory, system architecture, and functionalities. IEEE Comput 25(5):76–79CrossRef
44.
Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of random vector functional-link net. Neurocomputing 6:163–180CrossRef
45.
Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley, Reading, MAMATH
46.
Park J, Sandberg IW (1991) Universal approximation using radialbasis-function networks. Neural Comput 3:246–257CrossRef
47.
Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242CrossRef
48.
Qiao L, Sato M, Takeda H (1995) Learning algorithm of environmental recognition in driving vehicle. IEEE Trans Syst Man Cybern 25(6):917–925CrossRef
49.
Ranney TA, Garrott WR, Goodman MJ (2001) NHTSA driver distraction research: past, present, and future, National Highway Traffic Safety Administration, pp 1–8
50.
Regan MA, Hallet C, Gordon CP (2011) Driver distraction and driver inattention: definition, relationship and taxonomy. Accid Anal Prev J 43:1771–1781CrossRef
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 318–362
52.
Rupp GL (2010) Performance metrics for assessing driver distraction: the quest for improved road safety. SAE International, WarrendaleCrossRef
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)
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, Germany
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 Netherlands
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)
57.
Tikhonov AN (1963) Solution of incorrectly formulated problems and the regularization method. Soviet Math 4:1035–1038MATH
58.
Tikhonov AN, Arsenin VY (1977) Solutions of ill-posed problems. Winston, WashingtonMATH
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 Press
60.
Wickens CD (2002) Multiple Resources and performance prediction. Theor Issues Ergon Sci 3(2):159–177CrossRef
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):574–582CrossRef
62.
Xiang C, Ding SQ, Lee TH (2005) Geometrical interpretation and architecture selection of MLP. IEEE Trans Neural Netw 16(1):84–96CrossRef
63.
Young K, Regan M (2007) Driver distraction: a review of the literature. Distracted driving. Australian College Road Safety, Sydney, pp 379–405
64.
Yu D, Deng L (2012) Efficient and effective algorithms for training single-hidden-layer neural networks. Pattern Recogniti Lett 33:554–558CrossRef
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):805–821CrossRef
Metadata
Title
Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training
Authors
Marco Botta
Rossella Cancelliere
Leo Ghignone
Fabio Tango
Patrick Gallinari
Clara Luison
Publication date
14-02-2019
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 3/2019
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-019-01339-0

Other articles of this Issue 3/2019

Knowledge and Information Systems 3/2019 Go to the issue

Regular Paper

Enriching behavior patterns with learning styles using peripheral devices

Regular Paper

Information theoretic-PSO-based feature selection: an application in biomedical entity extraction

Regular Paper

A trust-based detection scheme to explore anomaly prevention in social networks

Survey Paper

The big data system, components, tools, and technologies: a survey

Regular Paper

A deep transfer learning approach for improved post-traumatic stress disorder diagnosis

Survey Paper

Tour recommendation and trip planning using location-based social media: a survey

Premium Partner

    Image Credits