Smart GNSS Integrity Monitoring for Road Vehicles: An Overview of AI Methods
Integrity monitoring is a key criterion for achieving robust and safe navigation systems. This work explores two integrity frameworks: the classical methods and their respective evolution towards the road vehicle urban scenario, and the artificial intelligence-based methods, where the monitoring pro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10854211/ |
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| Summary: | Integrity monitoring is a key criterion for achieving robust and safe navigation systems. This work explores two integrity frameworks: the classical methods and their respective evolution towards the road vehicle urban scenario, and the artificial intelligence-based methods, where the monitoring process is accomplished by data analysis and learning techniques. In most cases, machine learning outperforms traditional models, which are often observed under controlled, non-real-time conditions, by employing simple algorithms that may have limited success in real-world applications. An overview is provided on how these algorithms have been used, including a comparison of their characteristics and performances, offering insights into how they can evolve and possible future directions to achieve more reliable solutions. |
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| ISSN: | 2169-3536 |