CRAWLING：一种面向智能停车的车轮上众包算法
CRAWLING: a Crowdsourcing Algorithm on Wheels for Smart Parking
摘要：
提出了一种基于智能泊车的Wheels寻源算法CRAWLING的原理性设计。CRAWLING是一种用于互联汽车路由的车载服务。具体而言，配备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）{ \em众包}数据，以完成给定的路由任务。CRAWLING依赖于一个可靠的控制理论公式，它计算的路线是一个最优控制问题的解决方案，在这个问题中，汽车在跟踪一些期望的行为的同时，最大化捕捉环境条件的奖励。我们的服务的一个关键特征是，它允许考虑随机行为，同时考虑到异构数据流。我们提出了一个独立的，通用的，实现爬行，我们展示了它的有效性，旨在说明我们的服务的所有关键功能的一组场景。模拟表明，当汽车配备了CRAWLING时，该服务可以有效地协调车辆，使它们能够在线对道路状况做出反应，从而最大限度地降低其费用函数。实现我们的服务和复制数值结果的代码是公开提供的。
英文摘要：
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.
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