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Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Author

Listed:
  • Lin Wang

    (School of Transportation, Southeast University, Nanjing 211189, China
    Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Zhenhua Li

    (Research Institute of Highway Ministry of Transport, Beijing 100088, China
    Key Laboratory of Transport Industry of Intelligent Transportation Systems, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Qinglan Fan

    (Research Institute of Highway Ministry of Transport, Beijing 100088, China
    Key Laboratory of Transport Industry of Intelligent Transportation Systems, Research Institute of Highway Ministry of Transport, Beijing 100088, China)

Abstract

With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

Suggested Citation

  • Lin Wang & Zhenhua Li & Qinglan Fan, 2022. "Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8323-:d:857840
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    References listed on IDEAS

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    1. Min, Qingyun & Li, Junqiu & Liu, Bo & Li, Jianwei & Sun, Fengchun & Sun, Chao, 2021. "Guided model predictive control for connected vehicles with hybrid energy systems," Energy, Elsevier, vol. 230(C).
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