Approximately Orchestrated Routing and Transportation Analyzer: Large-scale traffic simulation for autonomous vehicles
Approximately Orchestrated Routing and Transportation Analyzer: Large-scale traffic simulation for autonomous vehicles
复制标题
近似编排的路线和运输分析器：自动驾驶车辆的大规模交通模拟
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
P. Stone
中科院分区：
文献类型：
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作者：
D. Carlino;Mike Depinet;Piyush Khandelwal;P. Stone
近年来，自动驾驶汽车取得了很大的进步，这种汽车现在比以往任何时候都更接近商业化。无人驾驶汽车的出现为以前所未有的方式优化交通提供了机会。本文介绍了一个开源的多智能体微观交通模拟器称为AORTA，它代表近似的路由和运输分析器，旨在优化在城市范围内的自主交通。AORTA通过使用OpenStreetMap（OSM）公开的道路数据生成地图，创建真实的世界的比例模拟。这允许通过AORTA在几分钟内为世界上任何地方的所需区域设置模拟。AORTA允许通过为各个驾驶员代理创建智能行为和这些代理遵循的交叉口策略来优化交通。这些行为和策略定义了代理如何相互交互，控制它们何时穿过交叉路口，以及将代理路由到目的地。本文演示了一个简单的应用程序，使用AORTA通过实验测试交叉口政策在城市范围内。
Autonomous vehicles have seen great advancements in recent years, and such vehicles are now closer than ever to being commercially available. The advent of driverless cars provides opportunities for optimizing traffic in ways not possible before. This paper introduces an open source multiagent microscopic traffic simulator called AORTA, which stands for Approximately Orchestrated Routing and Transportation Analyzer, designed for optimizing autonomous traffic at a city-wide scale. AORTA creates scale simulations of the real world by generating maps using publicly available road data from OpenStreetMap (OSM). This allows simulations to be set up through AORTA for a desired region anywhere in the world in a matter of minutes. AORTA allows for traffic optimization by creating intelligent behaviors for individual driver agents and intersection policies to be followed by these agents. These behaviors and policies define how agents interact with one another, control when they cross intersections, and route agents to their destination. This paper demonstrates a simple application using AORTA through an experiment testing intersection policies at a city-wide scale.