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

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.