Structure evolution of dynamic Bayesian network for traffic accident detection

Abstract

Recently, Bayesian network has been widely used to cope with the uncertainty of real world in the field of artificial intelligence. Dynamic Bayesian network, a kind of Bayesian network, can solve problems in dynamic environments. However, as node and state values of node in Bayesian network grow, it is very difficult to define structure and parameter of Bayesian network. This paper proposes a method which generates and evolves structure of dynamic Bayesian network to deal with uncertainty and dynamic properties in real world using genetic algorithm. Effectiveness of the generated structure of dynamic Bayesian network is evaluated in terms of evolution process and the accuracy in a domain of the traffic accident detection.