论文标题
爬行：在车轮上的众包算法，用于智能停车
CRAWLING: a Crowdsourcing Algorithm on Wheels for Smart Parking
论文作者
论文摘要
我们介绍了爬行的原则设计：在车轮上用于智能停车的众包算法。爬行是一项用于路由连接汽车的车内服务。具体来说，配备了我们服务的汽车能够从第三方（包括其他汽车，行人，智能传感器和社交媒体）中获得{\ em CrowdSource}数据，以完成给定的路由任务。爬行依赖于固体控制理论公式及其计算的途径是最佳控制问题的解决方案，在该问题中，汽车在跟踪某些所需的行为的同时最大程度地捕获了奖励捕获环境条件。我们服务的一个关键特征是，它允许考虑随机行为，同时考虑到异质数据的流。我们提出了一个独立的，通用的爬行实施，我们在旨在说明我们服务的所有关键特征的一组场景上展示了其有效性。模拟表明，当汽车配备爬行时，该服务有效地协调了车辆，使它们能够在线对道路状况做出反应，从而最大程度地减少其成本功能。实施我们的服务并复制数值结果的代码将公开可用。
We present the principled design of CRAWLING: a CRowdsourcing Algorirthm on WheeLs for smart parkING. CRAWLING is an in-car service for the routing of connected cars. Specifically, cars equipped with our service are able to {\em crowdsource} data from third-parties, including other cars, pedestrians, smart sensors and social media, in order to fulfill a given routing task. CRAWLING relies on a solid control-theoretical formulation and the routes it computes are the solution of an optimal control problem where cars maximize a reward capturing environmental conditions while tracking some desired behavior. A key feature of our service is that it allows to consider stochastic behaviors, while taking into account streams of heterogeneous data. We propose a stand-alone, general-purpose, implementation of CRAWLING and we show its effectiveness on a set of scenarios aimed at illustrating all the key features of our service. Simulations show that, when cars are equipped with CRAWLING, the service effectively orchestrates the vehicles, making them able to react online to road conditions, minimizing their cost functions. The code implementing our service and to replicate the numerical results is made openly available.
