Gligor Kotushevski

Intelligent Agent Model for Autonomous Navigation: Intelligent Driver Agent Model for Autonomous Navigation in a Computer Simulated Vehicular Traffic Network Gligor Kotushevski

Over the last few years, the vehicular traffic in the big cities has become over-congested. This has led to people wasting valuable time remaining in traffic jams. Traditional approaches to solve our traffic problems are no longer feasible. We cannot add more lanes to already multiple lane roadways because the costs of this approach are just too big. The purpose of this study was to investigate the possibilities of automating vehicular traffic and decrease traffic congestion by developing an intelligent driver agent model that autonomously navigates through a computer simulated traffic network. The aim was to examine various path-finding algorithms and cost evaluation functions through different traffic conditions so that a basic intelligent driver agent model is designed using the best combination of algorithms and cost functions found. The results gathered and their analysis should help shed some light on the usability of the various path-finding algorithms and cost evaluation functions for the purpose of creating an intelligent autonomous traffic agent, and should be especially useful to professionals in the Computer Simulation and Artificial Intelligence fields.