AI-Enabled Fingerprinting and Crowdsource-Based Vehicle Localization for Resilient and Safe Transportation Systems

Rathin Chandra Shit, Suraj Sharma, Kumar Yelamarthi, Deepak Puthal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The localization accuracy is critical for the development of future autonomous systems and location-based services. The accuracy level for localization is difficult to achieve in the case of urban and GPS denied environments due to high scattering. Fingerprint-based localization techniques promise to address these challenges. However, this technique demands to build a radio map before localization, which is a time-consuming and labor-intensive task. This article designs a crowd-sourced based localization system to address the radio map building problem in fingerprinting localization system. In this method, the first initial radio map is constructed from the path-loss RSS model, followed by the update of the fingerprints with crowd-sourcing. Finally, the vehicle location is estimated from the RSS sample by matching it with an updated radio map with a deep learning algorithm. The main advantage of the proposed approach is the calibration-free crowd-sourced fingerprint generation and its applicability in various location-based services in urban infrastructure.

Original languageEnglish
Article number9345371
Pages (from-to)4660-4669
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number7
DOIs
StatePublished - Jul 2021

Keywords

  • Deep learning
  • LBS
  • Markov Model
  • fingerprinting
  • intelligent transport system (ITS)
  • localization
  • signal processing

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