Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
117 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

GNSS Measurement-Based Context Recognition for Vehicle Navigation using Gated Recurrent Unit (2404.13955v1)

Published 22 Apr 2024 in eess.SP

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (19)
  1. Mounir Adjrad and Paul D Groves “Intelligent urban positioning using shadow matching and GNSS ranging aided by 3D mapping” In Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), 2016, pp. 534–553
  2. “SatProbe: Low-energy and fast indoor/outdoor detection based on raw GPS processing” In IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017, pp. 1–9 DOI: 10.1109/INFOCOM.2017.8057095
  3. “Empirical evaluation of gated recurrent neural networks on sequence modeling” In arXiv preprint arXiv:1412.3555, 2014
  4. “Deep Learning-Based Scenario Recognition with GNSS Measurements on Smartphones” In IEEE Sensors Journal, 2022, pp. 1–1 DOI: 10.1109/JSEN.2022.3230213
  5. “Context recognition and ubiquitous computing in smart cities: a systematic mapping” In Computing 103.5 Springer Vienna, 2021, pp. 801–825 DOI: 10.1007/s00607-020-00878-7
  6. Florent Feriol, Damien Vivet and Yoko Watanabe “A review of environmental context detection for navigation based on multiple sensors” In Sensors (Switzerland) 20.16, 2020, pp. 1–30 DOI: 10.3390/s20164532
  7. Han Gao and Paul D. Groves “Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone” In Navigation, Journal of the Institute of Navigation 65.1, 2018, pp. 99–116 DOI: 10.1002/navi.221
  8. Paul D. Groves “Shadow matching: A new GNSS positioning technique for urban canyons” In Journal of Navigation 64.3, 2011, pp. 417–430 DOI: 10.1017/S0373463311000087
  9. “I hear, therefore I know where I am: Compensating for GNSS limitations with cellular signals” In IEEE Signal Processing Magazine 34.5, 2017, pp. 111–124
  10. Sheng Liu “NMEA Dataset for Navigation Context Recognition:https://github.com/liusheng2020/nmeadatasetncr” Accessed: 2023-03-01 GitHub, https://github.com/liusheng2020/nmeadatasetncr, 2023
  11. Alex Sherstinsky “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network” In Physica D: Nonlinear Phenomena 404 Elsevier, 2020, pp. 132306
  12. Shan Suthaharan “Support vector machine” In Machine learning models and algorithms for big data classification Springer, 2016, pp. 207–235
  13. “Attention is all you need” In Advances in neural information processing systems 30, 2017
  14. “A seamless navigation system and applications for autonomous vehicles using a tightly coupled GNSS/UWB/INS/map integration scheme” In Remote Sensing 14.1, 2022 DOI: 10.3390/rs14010027
  15. “Urban environment recognition based on the GNSS signal characteristics” In Navigation 66 Wiley Online Library, 2019, pp. 211–225
  16. “Recurrent neural network based scenario recognition with multi-constellation GNSS measurements on a smartphone” In Measurement 153 Elsevier, 2020, pp. 107420
  17. Lotfi A Zadeh “Fuzzy sets” In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh World Scientific, 1996, pp. 394–432
  18. “Overview of Environment Perception for Intelligent Vehicles” In IEEE Transactions on Intelligent Transportation Systems 18.10, 2017, pp. 2584–2601 DOI: 10.1109/TITS.2017.2658662
  19. “Indoor/outdoor switching detection using multisensor densenet and LSTM” In IEEE Internet of Things Journal 8.3 IEEE, 2020, pp. 1544–1556
Citations (2)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com