GNSS Measurement-Based Context Recognition for Vehicle Navigation using Gated Recurrent Unit

TL;DR

This paper introduces a GNSS measurement-based context recognition method using a lightweight GRU network, achieving high accuracy in classifying diverse vehicle environments for improved navigation systems.

Contribution

It proposes the most detailed environment categorization framework to date and designs a new feature for better discrimination, combined with a real-time capable GRU-based recognition model.

Findings

Achieves 99.41% accuracy in isolated scenarios.

Attains 94.95% accuracy in transition scenarios.

Provides a publicly available large dataset for NCR research.

Abstract

Recent years, people have put forward higher and higher requirements for context-adaptive navigation (CAN). CAN system realizes seamless navigation in complex environments by recognizing the ambient surroundings of vehicles, and it is crucial to develop a fast, reliable, and robust navigational context recognition (NCR) method to enable CAN systems to operate effectively. Environmental context recognition based on Global Navigation Satellite System (GNSS) measurements has attracted widespread attention due to its low cost because it does not require additional infrastructure. The performance and application value of NCR methods depend on three main factors: context categorization, feature extraction, and classification models. In this paper, a fine-grained context categorization framework comprising seven environment categories (open sky, tree-lined avenue, semi-outdoor, urban canyon, viaduct-down, shallow indoor, and deep indoor) is proposed, which currently represents the most elaborate context categorization framework known in this research domain. To improve discrimination between categories, a new feature called the C/N0-weighted azimuth distribution factor, is designed. Then, to ensure real-time performance, a lightweight gated recurrent unit (GRU) network is adopted for its excellent sequence data processing capabilities. A dataset containing 59,996 samples is created and made publicly available to researchers in the NCR community on Github. Extensive experiments have been conducted on the dataset, and the results show that the proposed method achieves an overall recognition accuracy of 99.41\% for isolated scenarios and 94.95\% for transition scenarios, with an average transition delay of 2.14 seconds.