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How to best Automate Intersection Management

机译：如何最佳自动化交叉管理管理

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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 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.
机译：最近，对自主车辆的自适应控制系统进行了研究兴趣。本研究提出了对自动车辆多助理系统自动交叉管理的两种不同方法的比较评估。首先是使用自主交叉管理（AIM）系统的扩展来集中启发式控制方法。第二种是一种分散的神经演进方法，适应车辆控制器，因为它们共同导航交叉点。本研究测试了对互联交叉路路网络控制自动车辆组的两种方法，而无需交通信号灯或停止信号的约束。因此，这些任务环境模拟了车辆必须自主驱动的潜在未来情景，而无需特定的道路基础设施限制。测试各种方法的能力，以适当处理各种类型的互连交叉点，同时测试保持车辆的有效吞吐量并最小化延迟。结果表明，神经演进是一种自动化集体驾驶行为的有效方法，这些行为在广泛的道路网络上具有稳健，在那里进化控制器产生可比的任务性能或出局的AIM控制器。

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