A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling
A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling
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DOI：
10.1145/3369815
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发表时间：
2019-12
影响因子：
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通讯作者：
Weida Zhong;Qiuling Suo;Fenglong Ma;Yunfei Hou;Abhishek Gupta;C. Qiao;Lu Su
中科院分区：
文献类型：
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作者：
Weida Zhong;Qiuling Suo;Fenglong Ma;Yunfei Hou;Abhishek Gupta;C. Qiao;Lu Su
准确地描绘路面坑洼不仅有助于消除安全相关问题，提高驾驶员的通勤效率，还可以减少运输机构不必要的维护成本。在本文中，我们提出了一种基于智能手机的系统，能够精确估计坑洼的长度和深度，并介绍了坑洼数据收集、剖面聚合以及坑洼警告和报告的整体设计。该系统依靠车载智能手机的内置惯性传感器来估计坑洼轮廓，并警告驾驶员即将到来的坑洼。由于驾驶行为和车辆悬架系统的差异，构建此类系统的一个主要挑战是如何汇总来自多个参与车辆的相互冲突的感官报告。为了应对这一挑战，我们提出了一种新颖的可靠性感知数据聚合算法，称为可靠性自适应真相发现（RATD）。它推断每个数据源的可靠性，并以无人监督的方式聚合坑洞轮廓。我们的现场测试表明，所提出的系统可以有效地估计坑洞剖面，并且与流行的数据聚合方法相比，RATD算法显着提高了剖面精度。
Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone-based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built-in inertial sensors of vehicle-carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability-aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD). It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods.