title: A Bayesian Neural Network approach to estimating the Energy Equivalent Speed.
abstract: To reduce the number and the gravity of accidents, it is necessary to analyse and reconstruct them. Accident modelling requires the modelling of the impact which in turn requires the estimation of the deformation energy. There are several tools available to evaluate the deformation energy absorbed by a vehicle during an impact. However, there is a growing demand for more precise and more powerful tools. In this work, we express the deformation energy absorbed by a vehicle during a crash as a function of the Energy Equivalent Speed (EES). The latter is a difficult parameter to estimate because the structural response of the vehicle during an impact depends on parameters concerning the vehicle, but also parameters concerning the impact. The objective of our work is to design a model to estimate the EES by using an original approach combining Bayesian and Neural Network approaches. Both of these tools are complementary and offer significant advantages, such as the guarantee of finding the optimal model and the implementation of error bars on the computed output. In this paper, we present the procedure for implementing this Bayesian Neural Network approach and the results obtained for the modelling of the EES: our model is able to estimate the EES of the car with a mean error of 1.34 m s(-1). Furthermore, we built a sensitivity analysis to study the relevance of model's inputs.
authors.affiliation: Université de La Réunion, Laboratoire de Génie Industriel, Equipe Génie Civil et Thermique de l'Habitat, 15 avenue René Cassin, BP 7151, 97705 Saint-Denis Cedex, Ile de la Réunion, France. carine.riviere@uni-reunion.fr
authors.forename: C
authors.initials: C
authors.lastname: Riviere
authors.forename: P
authors.initials: P
authors.lastname: Lauret
authors.forename: J F Manicom
authors.initials: JF
authors.lastname: Ramsamy
authors.forename: Y
authors.initials: Y
authors.lastname: Page
mesh_terms: D000063:Accidents, Traffic; D001499:Bayes Theorem; D008962:Models, Theoretical; D009038:Motion; D016571:Neural Networks, Computer; D055585:Physical Phenomena; D010825:Physics