Knowledge modeling in context for intelligent car driving self-training system to improve careful, conscientious and contextualized drive

Patrick Brézillon 1 Juliette Brézillon 2
1 DECISION
LIP6 - Laboratoire d'Informatique de Paris 6
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Our goal is to improve drivers' awareness, i.e., the perception of the own driving from current events in the environment as well as anticipation into the close future. For that, we propose a methodology called careful, conscientious and contextualised drive (3C-DRIVE). The modelling of drivers' mental behaviour is obtained by a combination of local and global approaches. We obtain a twofold representation: a behaviour space and a situation space. Scenarios allow combining the two representations in a unified picture. The system thus aims to helping new drivers to contextualise their learned procedures into effective practices to avoid or correct risky behaviours by a self-training approach. This paper presents the methodology, data collection method and the implementation for the crossing of a T-crossroad.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01170481
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Submitted on : Wednesday, July 1, 2015 - 4:03:05 PM
Last modification on : Thursday, March 21, 2019 - 12:59:42 PM

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Patrick Brézillon, Juliette Brézillon. Knowledge modeling in context for intelligent car driving self-training system to improve careful, conscientious and contextualized drive. International Journal of Knowledge Engineering and Data Mining, 2010, 1 (1), pp.2-19. ⟨10.1504/IJKEDM.2010.032577⟩. ⟨hal-01170481⟩

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