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International audience; Contexte Dans les pays industrialisés, la population vieillit d'année en année. En France par exemple, on projette qu'en 2030, un habitant sur trois aura plus de 65 ans. S'impose alors la question de la préservation de l'autonomie, qui implique notamment la mobilité individuelle. La poursuite de la conduite automobile à un âge avancé est souvent un symbole fort du maintien de cette autonomie. Or conduire est une tâche complexe et exigeante, impliquant diverses facultés humaines parmi lesquelles certaines sont affectées par les effets de l'âge. D'un autre côté, les récentes innovations technologiques en matière d'assistance à la conduite, sous réserve qu'elles répondent bien aux besoins et aux caractéristiques des conducteurs âgés, sont susceptibles d'apporter un soutien bénéfique à cette population, permettant ainsi de garantir la sécurité routière et la mobilité de ce groupe d'usagers de la route. Objectifs Dans ce contexte, l'objectif de cette recherche est d'analyser l'activité de conduite de conducteurs âgés afin de déterminer et comprendre les spécificités et les difficultés potentielles de ces conducteurs. Il s'agit notamment d'observer et d'analyser les comportements de conduite en conditions naturelles afin d'évaluer les effets potentiels du vieillissement sur cette tâche complexe (difficultés rencontrées, risques d'erreurs spécifiques, mais aussi stratégies compensatoires mise en place), dans le but de spécifier de futurs dispositifs d'assistance ainsi que leur modalités d'interaction Homme-Machine, qui soient bien adaptés aux besoins réels des conducteurs âgés, en matière de sécurité routière comme de mobilité. Méthode Pour se faire, notre démarche scientifique s'inscrit dans une approche complémentaire alliant Ergonomie et Ingénierie Cognitive. D'une part, des données objectives de conduite sont collectées au moyen d'un véhicule instrumenté sur route ouverte. Ces mesures renseignent sur l'environnement du véhicule (caméras filmant les scènes avant et arrière, détection des panneaux de limitations de vitesse, dynamique des obstacles en aval du véhicule), sur sa dynamique (accélérations, vitesse, relevés sur le bus CAN), sur sa position (antenne GPS, position sur la voie) et sur les actions du conducteur (état des commandes

International audience; The aim of this research is to develop and implement a computational model able to simulate drivers' visual strategies in dual-task conditions and to investigate visual distraction effects. The modeling approach supporting this research is based on a cognitive model of the car driver so-called COSMODRIVE, focused on mental representations simulation (i.e. situational awareness) and implemented on a virtual platform (so-called SiVIC). In this framework, a module of the visual perception is needed for model interaction with the virtual road environment. This perception module is indeed the ''entry point'' for road scene analysis and decision-making. This modeling work is based on empirical data collected among 20 human drivers. Experiments have been designed in order to study visual distraction impact of driver's visual scanning. During this experiment, participants drove a car on simulator and answered at the same time to a visual secondary task. Visual strategies were collected through video films, and have been analyzed in order to model visual distraction effect. Different visual strategies for alternative scanning of the road scene and the pictogram screen (i.e. distraction in the cockpit) have been identified among our set of drivers. These empirical results have been then used to develop a visual perception module liable to dynamically explore the road environment, according to visual distraction effects. Through these empirical data, the perception module has been developed in order to reproduce these visuals strategies of human when a visual distraction occurs. The module is primarily based on a enhancement of a pre-existing virtual camera model in SiVIC platform, now used as a model of the human eye. The movement of gaze and target tracking can be simulated with the rotation of the camera depending on the location of the object to observe/perceive/discover. This visual scanning modeling could be interfaced with other numerical models of human driver in the future, according to the modular and portable approach used for this development. The visual perception module is connected with others modules of COSMODRIVE, with the aim to support the driver's mental representation elaboration and allow the model progression into the environment. But, in addition to visual thus scanning based on mental representations; the module can also simulate visual attention allocation to an on-board screen. In it possible to use the driver model for investigating driving errors due to a visual distraction while driving.

The aim of this research program, so-called COSMODRIVE (for Cognitive Simulation Model of the DRIVEr), is to develop and implement a cognitive simulation model of the car driver able to drive a virtual car in order to dynamically interact and progress into a virtual road environment (so-called SiVIC), through a dual "Perception-Cognition-Action" regulation loop based on both automatic processes (i.e. Skill-based behaviour and decisions) that can be performed in parallel without any attentional effort, and controlled processes (rule and knowledgebased behaviour or decision), requiring cognitive resources to be performed. The central cognitive components supporting this regulation loop in COSMODRIVE are drivers' mental representations of the road environment. These mental models are formulated in working memory (from a matching process between operative knowledge - the "Driving Schemas" and the "Envelope-Zones" - and the perceptive information extracted in the road scene through perception), and they correspond to the driver's Situation Awareness at a given time. Moreover, these mental models are used for anticipation of future situational states (i.e. expectations), in order to make decision an implement driving action allowing the driver to progress on the road and to safely interact with other road users. After having presented the research objectives and the theoretical background supporting this research, we will present some empirical data collected among human drivers in order to investigate visual distraction effects on driving performances and driver's Situational Awareness. Then, the forth section will more particularly present the recent model enhancement focused on Human error simulation, in terms of erroneous SA due to visual distraction while driving. Some empirical data collected among 20 human drivers will be briefly introduced, and a typical case of human errors simulation due to visual scanning of an additional screen will be presented (crash resulting to an erroneous/ not updated SA and a too late detection of a lead car breaking). The conclusion will explore the next steps of this research.

International audience; We present a methodology and a tool for analyzing the activity of an operator interacting with a complex technical device. The goal is of refining cognitive models of the operator by relating them to patterns of behavior in real situations. The activity is observed to be modeled as a trace having a graph structure. The trace is transformed according to a "use model" in order to become meaningful in the context of modeling theories. Our "Trace Based System" thus gathers both a representation of the activity and of the analyst's expertise for facilitating the discovery of knowledge in the field of cognitive psychology. The approach is illustrated by its application for car driver cognitive modeling.