Improving driver's situation awareness.

Juliette Brézillon (1) , Patrick Brézillon (2) , Charles Tijus (3, 4)

1 MALIRE - Machine Learning and Information Retrieval
2 DECISION
3 CHART - Cognitions Humaine et ARTificielle
4 LUTIN - Laboratoire des Usages en Technologies d'Information Numériques

Initial training, which concludes by a driving license, is insufficient because new drivers do not know how to contextualize the learned procedures into effective practices. Our goal is to improve the driver's Situation Awareness, i.e. the way in which the driver perceives events in the environment, and the projection of their status in a close future. More precisely, our concern is the way in which the driver evaluates the criticality of a situation. First, we model drivers’ behavior along two approaches, that is, local and global approaches, in order to have a driver model as exhaustive as possible. Second, we model drivers in a twofold representation, a situation space (an objective representation given by a lattice) and a behavior space (a subjective representation given by a contextual graph). Scenarios connect the two representations. We present in this paper the results of a specific study on driver classification based on the two approaches.

Mots clés: driver's

Domaines: Sciences de l'information et de la communication

Juliette Brézillon, Patrick Brézillon, Charles Tijus. Improving driver's situation awareness.. Fourth International Workshop on Modeling and Reasoning in Context (MRC-2007), Aug 2007, Roskilde, Denmark. pp.61-74. ⟨halshs-00630782⟩