Sensor Fusion For Avoidance

Sensor Fusion for Avoidance is a computational framework that integrates heterogeneous sensory data from multiple modalities to enhance the accuracy, robustness, and reliability of obstacle detection and avoidance decisions in dynamic environments.

38

Publications

1K

Citations

137

Authors

50

Institutions

Publications per year

2017–2026

18

Authors

137

  1. WE
    Wafa Elmannai

    Manhattan University, United States

    2 publications · 80 citations

    2

    h-index

  2. KE
    Khaled Elleithy

    University of Bridgeport, United States

    2 publications · 80 citations

    2

    h-index

  3. TH
    Tao Han

    Chengdu Normal University, China

    1 publications · 20 citations

    1

    h-index

  4. KL
    Kuan Liu

    University of Southern California, United States

    1 publications · 39 citations

    1

    h-index

  5. PL
    Peilin Liu

    Shanghai Jiao Tong University, China

    1 publications · 11 citations

    1

    h-index

1–5 of 137

Venues

  1. 2

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  2. 1

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  3. 1

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  4. Journal of Sensors

    1 publications · 10 citations

    1

    h-index

  5. 1

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1–5