Prediction of driver’s workload by means of fuzzy techniques

Pellegrino, Orazio

doi:10.3846/bjrbe.2012.17

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…In previous experiences (Bosurgi et al 2010(Bosurgi et al , 2013a(Bosurgi et al , 2013bPellegrino 2009Pellegrino , 2011Pellegrino , 2012aPellegrino , 2012b, we already verified a close correlation between road geometry, environment and drivers' behaviour.…”

Section: Aim Of the Researchmentioning

confidence: 64%

A Procedure for Evaluating the Influence of Road Context on Drivers’ Visual Behaviour

2016

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Abstract. In this paper, we investigated drivers' visual behaviour while travelling a road regularly opened to traffic in order to evaluate the effectiveness of the traditional scientific models and propose, at the same time, further measures useful for understanding the complex phenomenon. As is known, drivers acquire the necessary information for knowing the road geometry by visually detecting certain areas of the surrounding context. Some models in the literature have shown in a simple and convincing way these mechanisms, but they are valid only with specific assumptions, often very restrictive, such as a two-lane road, horizontal sign clearly visible and no interaction with other vehicles. For this reason, in this study we wanted to investigate different conditions, by estimating the visual strategy of some regular drivers on a three-lane road in presence of other vehicles. The visual behaviour was surveyed with the Tobii Glasses Eye Tracker and the resulting raw data were further manipulated by us to extract more useful information for our purposes. In particular, we quantified the driver's dedicated attention to the various elements present inside the environmental context, both static (road edges, road signs, dashboard, etc.) and dynamic (other vehicles), meaning by this term those that could potentially collide with the trajectories of our vehicle. The achieved results, highlighting the limits of validity of some recent studies, contain some proposed indexes useful to give a better understanding of the visual behaviour in order to detect any eventual weakness of the road.

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Section: Aim Of the Researchmentioning

confidence: 64%

A Procedure for Evaluating the Influence of Road Context on Drivers’ Visual Behaviour

2016

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“…To know further information about the trials and the consequent computations, we included in the bibliography all the necessary references [23][24][25][26][27].…”

Section: Application and Resultsmentioning

confidence: 99%

A Nonlinear Model to Predict Drivers’ Track Paths Along a Curve

2016

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This study proposes a prediction model about the trajectories a vehicle, in isolated conditions, along a curve of a road. As we know, the road environment induces stress on users and, under certain conditions, influences driving behavior. It is of advantage then, to isolate and identify those conditions from among the numerous variables, which are actually the most significant so as to prevent or mitigate the occurrence of dangerous maneuvers. On the basis of an experiment performed using an instrumented vehicle, we collected a data base to which we subsequently applied Neuro-Fuzzy techniques for the selection of the most representative variables. We then used these data to prepare a nonlinear dynamic Hammerstein-Wiener's model able to predict the track paths along curves. The findings were encouraging since almost all the results obtained from the validation checks proved satisfactory. This research is the first step in the identification of complex systems and could be applied in road safety measures and design of new and existing roads.

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“…To know further information about the trials and the consequent computations, all the necessary references were included in the bibliography (Antov et al, 2007;Bosurgi et al, 2010Bosurgi et al, , 2013Gonzales & Woods, 2001;Pellegrino, 2009Pellegrino, , 2012.…”

Section: Methodsmentioning

confidence: 99%

“…The output variable is represented by an index called context information (CI), which we developed in previous documents (Bosurgi et al, 2010(Bosurgi et al, , 2013Pellegrino, 2009Pellegrino, , 2012 depending on the movement of the head-eyes system during the driving task.…”

Section: Purpose Of the Activitymentioning

confidence: 99%

Prediction of Drivers’ Visual Strategy Using an Analytical Model

Bosurgi

D’Andrea

Pellegrino

2014

Journal of Transportation Safety & Security

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The proposed research addresses the problem of predicting driver's visual behavior. Notably, this issue is of great importance for road safety because it provides information about the true perception of the road environment. This need is particularly felt when the real situation is so complex that it cannot be represented by simple analytical audits contained in the road international standard. Therefore, with the help of an instrumented vehicle, the authors conducted some trials to identify relevant data useful to recognize the road environment as perceived by drivers. Based on this data, the authors prepared a linear model that, properly calibrated, would enable them to predict the drivers' visual strategy. The results were encouraging, because a model so simple in structure as well as in the number of variables involved could control a very complex phenomenon, such as that of visual perception. The proposed methodology can be applied as a further control in the design of new roads and in the maintenance of existing ones and, in particular, in the analysis of special scenarios (e.g., unusual conditions of weather or traffic) that are not represented by ordinary audits contained in the road standards.

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Prediction of driver’s workload by means of fuzzy techniques

Cited by 15 publications

References 24 publications

A Procedure for Evaluating the Influence of Road Context on Drivers’ Visual Behaviour

A Procedure for Evaluating the Influence of Road Context on Drivers’ Visual Behaviour

A Nonlinear Model to Predict Drivers’ Track Paths Along a Curve

Prediction of Drivers’ Visual Strategy Using an Analytical Model

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