Driving behaviour is a direct consequence of the stimuli received from the road infrastructure, from the surrounding environment and from the atmosphere inside the vehicle. Almost all of these perceptions, which affect steering, are received through the drivers' eyes. For this reason, in this paper visual behaviour was examined in order to deduce new indexes connected to mental workload. To this aim, a consistent sample of drivers covered a rural road inside a vehicle, while their eye movements and driving activities were being recorded with suitable instruments. The quantification of some variables involved in the trials permitted the evaluation of visual behaviour and determination of a measure of mental workload, identifying also any situations where performance was compromised. Some reports between mental workload and road geometry, summarized in a few tables, could represent a further aid for road designers and managers.
This paper deals with the analysis of the main variables involved in the visual activity of a driver of motor vehicles, in order to identify the most important quantities and implement, therefore, appropriate corrective actions to the achievement of road safety. The first step in this research was to survey a number of variables within the road environment and processing this data base with clustering techniques in order to extract useful information for purpose. In this case, a mixture of procedures based on Fuzzy Logic (FL) and Artificial Neural Network (ANN) were applied not only to extract knowledge not known a priori but, above all, to define the membership functions and rules of the fuzzy model without recourse to the skills of the analyst, not always so objective. This procedure, applied to a rural road open to traffic, showed a good performance in predicting the user's visual behavior and, especially, in identifying the most influential variables. This aspect may allow the agency to direct the maintenance operations so that to facilitate understanding of the information contained within the road environment, thus improving safety.
The driver, through sight, acquires a lot of information from the road environment, most of which is necessary for his safe route. However, if the amount of information per unit of time is excessive, potentially dangerous situations of overload could be created. Even the opposite condition, that of a road that does not adequately stimulate the cognitive functions of the driver, may pose certain safety problems because it triggers the so-called boredom effect. This phenomenon, generally classified under the name of workload, was treated with great depth in literature but, probably, sufficiently detailed methodology has not yet been proposed for making forecasts on this variable along the road. The difficulty of preparing a reliable model can be explained by some of the characteristics of the road environment: many uncertain variables, including the human factor, choosing the most appropriate analytical method, lack of appropriate databases. The purpose of this article, therefore, is to present a prediction model based on the analysis of physiological workload by means of head-eyes movements and fuzzy techniques applied to a real context. The results obtained, although limited by the observed data set, allowed for the prediction with some accuracy of the tendency of the workload, referring also to the overload and under load thresholds position of which was defined on the basis of performance measurements along the road under consideration. In the first stage of the study the methodology is applied to the design of maintenance on an existing road, but once the correctness of the procedure is established, it can also be extended to new roads.
The current international road standards, in order to give organization and safety, promote the classification of roads according to their technical and functional characteristics beyond their administrative membership, but the procedures are yet strongly based on the expertise's judgment. In fact, although this activity has a great importance for the consequences that produces in terms of responsibility and allocation of economic resources, it is solely based on the quantification of some variables without specifying methods or analytical procedures. In this paper, after an instrumental survey of the road environment, we applied data mining techniques that consider the 'vagueness' of the analysed scenario. The type of algorithms used, therefore, permits to quantify a degree of membership (among 0 and 1) of a road to the groupings provided and to prepare any corrective action in order to direct the final result towards a specific class with greater precision. In addition, this method is very flexible and willing to contain new variables or observations at different times with great easiness. Moreover, the geographical location of the individual observations, as it was done also in this research, can be transferred to a GIS system, with a positive impact on maintenance programs.
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