title: Field tests and machine learning approaches for refining algorithms and correlations of driver's model parameters.
abstract: This paper describes the field tests on a driving simulator carried out to validate the algorithms and the 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' behavioural data have been 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 modelling and the tests conducted for the validation of these parameters. A test comparing predicted and expected outcomes of the modelled parameters for each machine learning technique has been carried out: for distraction, in particular, promising results (low prediction errors) have been obtained by adopting an artificial neural network.
authors.affiliation: Centro Ricerche Fiat, Strada Torino, Orbassano (TO), Italy. fabio.tango@crf.it
authors.forename: Fabio
authors.initials: F
authors.lastname: Tango
authors.forename: Luca
authors.initials: L
authors.lastname: Minin
authors.forename: Francesco
authors.initials: F
authors.lastname: Tesauri
authors.forename: Roberto
authors.initials: R
authors.lastname: Montanari
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention; D001334:Automobile Driving; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D008962:Models, Theoretical; D012106:Research