CPD:基于人群的坑洞检测

Florian Wirthmüller, Jochen Hipp, K. Sattler, M. Reichert
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引用次数: 4

摘要

路面上的坑洼和其他损坏构成了一个与道路一样古老的问题。尽管如此,坑洼仍然普遍存在,影响着乘客的驾驶舒适性和道路安全。如果人们知道坑洼的确切位置,就有可能有选择地修理它们,或者至少在修理之前警告司机。然而,这两种场景都需要对它们进行检测和定位。为此,我们提出了一种基于人群的方法,使尽可能多的已经在我们道路上行驶的车辆能够检测到坑洼并将其报告给集中的后端应用程序。虽然每辆车只提供有限和不精确的信息,但在大规模收集这些信息时,有可能更精确地确定这些信息。例如,这些更精确的信息可以用来警告后面的车辆前方有坑洼,以提高整体安全性和舒适性。在本工作中,对这一想法进行了检验,并实现了所需系统的离线可执行版本。此外,该方法还使用来自测试车队的真实传感器读数的大型数据库进行了评估,从而证明了其可行性。我们的调查显示,建议的持续专业培训方法有望通过改善驾驶舒适度和提高道路安全性为客户带来好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CPD: Crowd-based Pothole Detection
Potholes and other damages of the road surface constitute a problem being as old as roads are. Still, potholes are widespread and affect the driving comfort of passengers as well as road safety. If one knew about the exact locations of potholes, it would be possible to repair them selectively or at least to warn drivers about them up to their repair. However, both scenarios require their detection and localization. For this purpose, we propose a crowd-based approach that enables as many of the vehicles already driving on our roads as possible to detect potholes and report them to a centralized back-end application. Whereas each single vehicle provides only limited and imprecise information, it is possible to determine these information more precisely when collecting them at a large scale. These more exact information may, for example, be used to warn following vehicles about potholes lying ahead to increase overall safety and comfort. In this work, this idea is examined and an offline executable version of the desired system is implemented. Additionally, the approach is evaluated with a large database of real-world sensor readings from a testing fleet and therefore its feasibility is proved. Our investigation shows that the suggested CPD approach is promising to bring customers a benefit by an improved driving comfort and higher road safety.
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