A reliability-aware vehicular crowdsensing system for pothole profiling

Weida Zhong, Qiuling Suo, Fenglong Ma, Yunfei Hou, Abhishek Gupta, Chunming Qiao, Lu Su

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number160
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume3
Issue number4
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction

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