Paper
18 November 2022 A review of the application of machine learning technologies in vehicle navigation and positioning
Lewa Zheng, Jie Li, Xiaomei Qu, Fan Li
Author Affiliations +
Proceedings Volume 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022); 124731A (2022) https://doi.org/10.1117/12.2653447
Event: Second International Conference on Optics and Communication Technology (ICOCT 2022), 2022, Hefei, China
Abstract
In recent years, the accuracy requirement of vehicle navigation and positioning is higher and higher. Since some obvious disadvantages emerge in the integration of various traditional technologies, many studies have begun to apply machine learning to vehicle navigation and positioning, which utilize the powerful self-learning ability of machine learning algorithms. The main advantages of machine learning methods include solving the problem of narrow application scope of traditional information fusion algorithms. Solve the problems of low navigation and positioning accuracy and poor anti-interference ability. In this paper, the applications of machine learning related algorithms in vehicle navigation and localization are overviewed in detail, including support vector machines, neural networks and random forests. Meanwhile, the application research status of machine learning technology in vehicle navigation and positioning is summarized, and the future research directions are prospected.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lewa Zheng, Jie Li, Xiaomei Qu, and Fan Li "A review of the application of machine learning technologies in vehicle navigation and positioning", Proc. SPIE 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022), 124731A (18 November 2022); https://doi.org/10.1117/12.2653447
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KEYWORDS
Machine learning

Global Positioning System

Navigation systems

Neural networks

Evolutionary algorithms

Microelectromechanical systems

Data modeling

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