Urban Road Safety Prediction: A Satellite Navigation Perspective

Halim Lee, Jiwon Seo, Zaher M. Kassas

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure the safe driving of automated vehicles, a vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, and so on). Such information can be derived from roads' physical properties, the vehicle's capabilities, and weather conditions. From a GNSS-based navigation perspective, the reliability of GNSS signals in different locales, which is heavily dependent on the road layout within the surrounding environment, is crucial to ensure safe automated driving. An urban road environment surrounded by tall objects can significantly degrade the accuracy and availability of GNSS signals. This article proposes an approach to predict the reliability of GNSS-based navigation to ensure safe urban navigation. Satellite navigation reliability at a given location and time on a road is determined based on the probabilistic position error bound of the vehicle-mounted GNSS receiver. A metric for GNSS reliability for ground vehicles is suggested, and a method to predict the conservative probabilistic error bound of the GNSS navigation solution is proposed. A satellite navigation reliability map is generated for various navigation applications. As a case study, the reliability map is used in a proposed optimization problem formulation for automated ground vehicle safety-constrained path planning.

Original languageEnglish
Pages (from-to)94-106
Number of pages13
JournalIEEE Intelligent Transportation Systems Magazine
Issue number6
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2009-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


Dive into the research topics of 'Urban Road Safety Prediction: A Satellite Navigation Perspective'. Together they form a unique fingerprint.

Cite this