How to Best Automate Intersection Management

Parker, Aashiq and Nitschke, Geoff (2017) How to Best Automate Intersection Management, Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2017), San Sebastian, Spain, 1247-1254, IEEE Press.

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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 multiagent 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.

Item Type: Conference paper
Subjects: Computing methodologies > Artificial intelligence
Date Deposited: 23 Nov 2017
Last Modified: 10 Oct 2019 15:31

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