Discriminating cognitive processes with eye movements in a decision-making driving task.

An experiment was conducted in a driving simulator to test how eye-movements patterns evolve over time according to the decision-making processes involved in a driving task. Participants had to drive up to crossroads and decide to stop or not. The decision-making task was considered as the succession of two phases associated with cognitive processes: Differentiation (leading to a prior decision) and Consolidation (leading to a final decision). Road signs (Stop, Priority and GiveWay) varied across situations, and the stopping behavior (Go and NoGo) was recorded. Saccade amplitudes and fixation durations were analyzed. Specific patterns were found for each condition in accordance with the associated processes: high visual exploration (larger saccade amplitudes and shorter fixation durations) for the Differentiation phase, and lower visual exploration (smaller saccades and longer fixations) for the Consolidation phase. These results support that eye-movements can provide good indexes of underlying processes occurring during a decision-making task in an everyday context

Towards the development of a User Interface to model scenarios on driving Simulators

International audienceScenario Modeling on driving simulator requires careful consideration and controlled environment (depending on the research objectives) to achieve the desired goal of the experiment. It is one of the critical steps while designing and implementing an experiment on a driving simulator. It specifies where and what happens in the simulator by specifying, where to place the virtual objects and what those objects will be doing during the experimental trials. But complex and technical nature of driving simulator makes it difficult for the end-users (behavioral researchers/trainers) to design and execute and experimental protocol

A detailed description of a user-centered interface to model scenarios on driving simulator

International audienceModeling scenarios on driving simulators is a complex and difficult task for end-users because most of them usually don’t have the skills required to program the scenarios. In this paper, we present a User-centered solution in which we split the scenario modeling interface into 3 subinterfaces (Template Builder, Experiment Builder, Experiment Interface) based on user skills.We have developed a prototype of the interface, which is explained in detail

Filling the user skill gap using HCI techniques to implement experimental protocol on driving simulators

Programming activities are performed not only by programmers but also by end-users in order to support their primary goals in different domains and applications. End-users do not have formal training in programming, so interaction environment and systems are needed, which could account for user skills. The objective of our work is to fill the gap between the user skills and the goals they want to achieve using driving simulators. This paper presents the results of a research in which we have proposed a solution for the primary users of the driving simulator to design and implement experimental protocol. We have used the user-centered design (UCD) technique, conducted a user survey, and proposed a solution, in which we have categorized the Interface of the driving simulator into three sub-interfaces based on the skills of the users. These interfaces are Experiment Builder (Nontechnical persons), Template builder (for technical persons) and Experiment Interface (for any user to run as experiment). A prototype based on this concept is developed and some feedback were collected from end-users. Our results indicate that, users can implement an experimental protocol without having programming skills using our proposed design

Designing a Tone Mapping Algorithm for Road Visibility Experiments

National audienceRoad visibility studies can take strong benefit from the use of computer graphics images through driving simulation and psychovisual experiments. But display devices are unable to render the complex visual environment of the driver. A Tone Mapping (TM) algorithm aims to convert high dynamic radiance (HDR) images into images that can be displayed. The goal of our TM algorithm is to preserve the visual perception of the observer

Towards an Analytical Age-Dependent Model of Contrast Sensitivity Functions for an Ageing Society

The Contrast Sensitivity Function (CSF) describes how the visibility of a grating depends on the stimulus spatial frequency. Many published CSF data have demonstrated that contrast sensitivity declines with age. However, an age-dependent analytical model of the CSF is not available to date. In this paper, we propose such an analytical CSF model based on visual mechanisms, taking into account the age factor. To this end, we have extended an existing model from Barten (1999), taking into account the dependencies of this model’s optical and physiological parameters on age. Age-dependent models of the cones and ganglion cells densities, the optical and neural MTF, and optical and neural noise are proposed, based on published data. The proposed age-dependent CSF is finally tested against available experimental data, with fair results. Such an age-dependent model may be beneficial when designing real-time age-dependent image coding and display applications

Drivers’ visual attention: A field study at intersections

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Visual complexity of urban streetscapes: human vs computer vision

International audienceUnderstanding visual complexity of urban environments may improve urban design strategies and limit visual pollution due to advertising, road signage, telecommunication systems and machinery. This paper aims at quantifying visual complexity specifically in urban streetscapes, by submitting a collection of geo-referenced photographs to a group of more than 450 internet users. The average complexity ranking issued from this survey was compared with a set of computer vision predictions, attempting to find the optimal match. Overall, a computer vision indicator matching comprehensively the survey outcome did not clearly emerge from the analysis, but a set of perceptual hypotheses demonstrated that some categories of stimuli are more relevant. The results show how images with contrasting colour regions and sharp edges are more prone to drive the feeling of high complexity

A Model for Automatic Diagnostic of Road Signs Saliency

Abstract Road signs, the main communication media towards the drivers, play a significant role in road safety and traffic control through drivers' guidance, warning, and information. However, not all traffic signs are seen by all drivers, which sometimes lead to dangerous situations. In order to manage safer roads, the estimation of the legibility of the road environment is thus of importance for road engineers and authorities who aim at making and keeping traffic signs salient enough to attract attention regardless of the driver's workload. Our long term objective is to build a system for the automatic estimation of road sign saliency along a road network, from images taken with a digital camera on-board a vehicle. This system will be interesting for accident analysis and prevention since it will enable a fine diagnostic of the road signs saliency, helping the road manager decide on which signs he must act and how (replacement or background modification). This should lead to improved asset management, road infrastructure maintenance and road safety. What attracts driver's attention is related both to psychological factors (motivations, driving task, etc.) and to the photometrical and geometrical characteristics of the road scene (colours, background, etc.). The saliency (or conspicuity) of an object is the degree to which this object attracts visual attention for a given background. Road signs perception depends on the two main components of visual attention: objects pop-out and visual search. The first one is less relevant when the task is to search for a particular object, whereas one important part of the driving task is to look for road signs. As most of current computational models of visual search saliency are limited to laboratorysituations, we propose a new model to compute visual search saliency in natural scenes. Relying on statistical learning algorithms, the proposed algorithm emulates the priors a driver learns on object appearance for any given class of road signs. The algorithm performs both the detection of the object of interest in the image and the estimation of its saliency. The proposed computational model of saliency was evaluated through psycho-visual experiments. This opens the possibility to design automatic diagnostic systems for road signs saliency