Panoptes:有目标的众包汽车

Utsav Drolia, Kunal Mankodiya, Nathan D. Mickulicz, P. Narasimhan
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引用次数: 2

摘要

对于我们每天走过的道路和生活的环境,没有统一的、完整的、可量化的、颗粒状的、更新的信息来源。这导致了对路况的模糊,在正常情况下是可以容忍的,但在不利条件下,如雪阻塞、风暴导致的内涝、道路退化和坑洞,就会产生极大的问题。没有这些知识,城市当局就不能对这些问题采取有效的行动。此外,一个人在车里只知道自己周围的环境,而不知道接下来会发生什么。我们的方法是部署许多具有传感、计算和报告功能的嵌入式模块,每个模块都可以简单地插入任何车辆。因此,与静态传感器相比,这使得每辆车都能连接到云,覆盖范围更大。每个模块本身报告的数据可能容易出错。因此,云人群从这些模块中获取数据并将其合并,以增加对信息的信心。我们的工作,Panoptes,通过众包的城市道路坑洼检测展示了这些方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Panoptes: Crowd-sourced Cars with a Cause
There is no consolidated, integral, quantifiable, granular, updated source of information for the roads we traverse and the environment we live in everyday. This leads to ambiguity about road conditions, which is tolerable during normal conditions but extremely problematic in adverse conditions such as snow blockages, water-logging due to storms, degraded roads and potholes. Without such knowledge, city authorities cannot take effective action against such problems. Also, one only has knowledge about ones immediate surroundings in a car, and not what to expect further down the road. Our approach is to deploy a number of embedded modules capable of sensing, computing and reporting, each of which can simply be plugged into any vehicle. Hence this enables each vehicle's connectivity to the cloud and larger coverage as compared to static sensors. The data reported by each module itself might be prone to errors. Therefore, the cloud crowd sources the data from these modules and merges it to increase confidence in the information. Our work, Panoptes, demonstrates these aspects through crowdsourced pothole detection for city roads.
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