@misc{cogprints3757, title = {Proscriptive Bayesian Programming Application for Collision Avoidance}, author = {C Koike and C Pradalier and P Bessiere and E Mazer}, year = {2003}, url = {http://cogprints.org/3757/}, abstract = {Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr.} }