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Area of Science:

  • Engineering
  • Computer Science
  • Robotics

Background:

  • Precise vehicle positioning is critical for intelligent transportation and autonomous driving.
  • GPS-denied environments like urban canyons hinder continuous lane-level localization.

Purpose of the Study:

  • To develop a robust vehicle positioning strategy for urban scenarios using ultra-wideband (UWB) and low-cost sensors.
  • To overcome challenges posed by non-line-of-sight (NLOS) propagation and multipath interference in UWB signals.

Main Methods:

  • An autoregressive integrated moving average (ARIMA) model was employed to address the NLOS problem.
  • A tightly-coupled multi-sensor fusion algorithm with fuzzy calibration logic (FCL) was developed to adaptively mitigate UWB interferences.
  • The proposed strategy was evaluated through experimental testing.

Main Results:

  • The proposed system demonstrates the potential for accurate, reliable, and continuous vehicle localization.
  • The approach effectively mitigates NLOS and multipath interferences in complex urban settings.
  • Experimental results validate the system's performance regardless of GPS availability.

Conclusions:

  • The developed low-cost positioning strategy offers a viable solution for autonomous driving and intelligent transportation systems.
  • Adaptive sensor fusion and NLOS mitigation are key to achieving robust localization in challenging environments.
  • This approach enhances vehicle tracking capabilities in GPS-denied urban areas.