GNSS Measurement-Based Context Recognition for Vehicle Navigation using Gated Recurrent Unit
arxiv(2024)
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.
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