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Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching

Nur Uddin Javed et al.

IEEE Transactions on Intelligent Transportation Systems2026https://doi.org/10.1109/tits.2025.3648744preprint
ABDC A
Weight
0.37

Abstract

Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model’s prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.

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https://doi.org/https://doi.org/10.1109/tits.2025.3648744

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@article{nur2026,
  title        = {{Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching}},
  author       = {Nur Uddin Javed et al.},
  journal      = {IEEE Transactions on Intelligent Transportation Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tits.2025.3648744},
}

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Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.