Field Tests and Machine Learning Approaches for Refining Algorithms and Correlations of Driver’s Model Parameters

This paper describes the field tests on a driving simulator carried out to validate the algorithms and correlations of dynamic parameters, specifically driving task demand and drivers’ distraction, able to predict drivers’ intentions. These parameters belong to the driver's model developed by AIDE (Adaptive Integrated Driver-vehicle InterfacE) European Integrated Project. Drivers’ behavioral data was collected from the simulator tests to model and validate these parameters using machine learning techniques, specifically the adaptive neuro fuzzy inference systems (ANFIS) and the artificial neural network (ANN). Two models of task demand and distraction have been developed, one for each adopted technique. The paper provides an overview of the driver's model, the description of the task demand and distraction modeling and the tests conducted for the validation of these parameters. A test comparing predicted and expected outcomes of the modeled parameters for each machine learning technique has been carried out: for distraction, in particular, promising results (low prediction errors) were obtained by adopting an artificial neural network.

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  • Supplemental Notes:
    • Abstract reprinted with permission from Elsevier
  • Authors:
    • Tango, Fabio
    • Minin, Luca
    • Tesauri, Francesco
    • Montarari, Roberto
  • Publication Date: 2010-3


  • English

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  • Accession Number: 01148096
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 5 2010 1:21PM