Risk Assessment Algorithms Based On Recursive Neural Networks

Abstract : The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety systems
Type de document :
Communication dans un congrès
International Joint Conference On Neural Networks - IJCNN 2007, Aug 2007, Orlando, Florida/ USA, 2007
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Contributeur : Alejandro Chinea Manrique de Lara <>
Soumis le : vendredi 4 mai 2007 - 12:03:04
Dernière modification le : vendredi 25 mai 2018 - 12:02:03
Document(s) archivé(s) le : mardi 21 septembre 2010 - 13:34:56


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  • HAL Id : inria-00129074, version 2
  • ARXIV : 0705.0602



Alejandro Chinea Manrique de Lara, Michel Parent. Risk Assessment Algorithms Based On Recursive Neural Networks. International Joint Conference On Neural Networks - IJCNN 2007, Aug 2007, Orlando, Florida/ USA, 2007. 〈inria-00129074v2〉



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