Urban Road Safety Prediction: A Satellite Navigation Perspective

TL;DR

This paper presents a method to predict GNSS signal reliability in urban environments to enhance the safety of automated vehicle navigation, accounting for environmental factors affecting signal accuracy.

Contribution

It introduces a probabilistic approach to assess GNSS reliability and generates navigation reliability maps for safe urban autonomous driving.

Findings

Reliability maps improve path planning safety.

Probabilistic error bounds effectively predict GNSS signal degradation.

Method enhances urban navigation safety for automated vehicles.

Abstract

Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure safe driving of automated vehicles, the vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, etc.). Such information can be derived from the roads' physical properties, 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 the proposed optimization problem formulation for automated ground vehicle safety-constrained path planning.