Risk Assessment Algorithms Based on Recursive Neural Networks

— 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 express...

• Type: Conference
• Year: 2007
• Where: IJCNN
• Authors: Alejandro Chinea Manrique De Lara, Michel Parent
• Keywords: IJCNN 2007, Information Encoding Procedure, Recursive Neural Networks, Road Intersection