Graphical model-based recursive motion prediction planning algorithm in stochastic dynamic environment

Abstract

Various types of autonomous vehicles(AVs) are used widely in the field of military and civilian. Aiming at the difficulty of the real-time intelligent planning of the AVs in the dynamic and uncertain complex environment, a more generalized graphical model-based planning frame and algorithm is studied in this paper. To plan the waypoints for AVs in stochastic environment, a dynamic Bayesian network-based recursive motion prediction planning (RMPP) algorithm is designed. The uncertainty object model and the dynamic utility function have been analyzed. Dynamic Bayesian network, which is one of the graphical models, has been verified to predict the mobile target status. RMPP helps to convert an uncertainty optimization into a deterministic problem with optimizing the waypoints allocation under the constraints which maximizes the utility score in dynamic environment. This approach is implemented and tested on the autonomous vehicle path planning problem. Experimental results demonstrate a substantial effectiveness in computation cost.