How to best Automate Intersection Management

Abstract

Recently there has been increased research interest in developing adaptive control systems for autonomous vehicles. This study presents a comparative evaluation of two distinct approaches to automated intersection management for a multi-agent system of autonomous vehicles. The first is a centralized heuristic control approach using an extension of the Autonomous Intersection Management (AIM) system. The second is a decentralized neuro-evolution approach that adapts vehicle controllers so as they collectively navigate intersections. This study tests both approaches for controlling groups of autonomous vehicles on a network of interconnected intersections, without the constraints of traffic lights or stop signals. These task environments thus simulate potential future scenarios where vehicles must drive autonomously without specific road infrastructure constraints. The capability of each approach to appropriately handle various types of interconnected intersections, while maintaining an efficient throughput of vehicles and minimizing delay is tested. Results indicate that neuro-evolution is an effective method for automating collective driving behaviors that are robust across a broad range of road networks, where evolved controllers yield comparable task performance or out-perform an AIM controller.