Description du projet
Une technologie d’IA pour prévenir les erreurs humaines avant qu’elles ne surviennent
Certaines erreurs ne peuvent être corrigées même si elles sont détectées à temps; beaucoup d’autres sont irréversibles. Il est crucial de reconnaître les erreurs humaines avant qu’elles ne se produisent, car le coût financier qui en découle est énorme. Le projet FORHUE du Conseil européen de la recherche vise à développer des méthodes basées sur l’IA pour prédire le mouvement des humains et des objets avec lesquels ils interagissent, à partir de données vidéo. L’accent est mis sur les actions humaines non intentionnelles qui peuvent être reconnues avant qu’elles ne surviennent. Un modèle génère un retour auditif si une erreur est prévue. Le retour d’information guidera également les utilisateurs pour mener à bien leur action planifiée. Pour finir, le projet simulera la façon dont les êtres humains réagissent à ce retour d’information.
Objectif
Human errors remain the main source of incidents. They can lead to fatalities, traffic accidents, or product defects and cause high economic and social cost. While some errors can still be corrected if they are detected in time, many human errors cause high costs as soon as they occur or are even irreversible. In these cases, it is very important to recognize human errors before they occur.
The goal of this project is therefore to develop methods based on artificial intelligence that forecast human errors from video data. We focus on erroneous and unintentional human actions and we aim to support humans to avoid them. In order to achieve this goal, we aim to solve three tasks jointly. We aim to develop methods that forecast human motion and intention with a very low latency such that unintentional actions can be recognized before they occur. Without the capability to interfere, however, even the best forecasting model does not prevent human errors. We therefore aim to develop a model that generates an auditory feedback if an error is forecast. The feedback, however, should not only warn humans, but also guide them such that they can successfully complete their intended action. Finally, we aim to model how humans will react to the feedback.
We thus aim to develop a model that forecasts the motion of humans and objects they interact with, that recognizes human errors before they occur, and that guides the human motion via auditory feedback in order to prevent errors. The model should automatically decide if and what auditory feedback is generated by reasoning how the feedback will affect the motion of persons that are close-by. While we aim to showcase that the developed technology is able to prevent errors before they occur, this technology has the potential to drastically reduce the social and economic costs caused by human errors in the long term.
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
53113 Bonn
Allemagne