Agent-Based Modelling of Interacting Unmanned Vehicles Dynamics with the FLAME GPU

Abstract

The article presents an approach to modelling traffic flows based on the intelligent analysis of unmanned ground vehicles (UGVs) dynamics within of which a novel decision-making system on maneuvering for agent-UGVs is designed. The proposed system uses clustering methods for an estimation of a traffic congestion density on alternative routes of the digital road network (DRN) to detour problem areas and minimisе risks of traffic accidents appearing. For the first time, the spatial dynamics of interacting UGVs is modelled with complex schemes for constructing the DRNs, including multiple intersecting ring, straight and diagonal sections that form various alternative routes for road users. The de­veloped model is implemented with the supercomputer agent-based modelling framework FLAME GPU. An algorithm for the behaviour of UGVs during the formation of traffic congestions has been developed, in particular, it provides an effective splitting of traffic flows by increasing the probability of choosing less pressed alternative routes for UGVs. As a result of numerical experiments, the following important regularity was obtained: an increase in the share of UGVs in the DRN using intelligent maneuvering based on cluster analysis of traffic flows with a controlled radius of an agent personal space provides a decrease in the total density of traffic congestion and contributes to a decrease in accidents.