Abstract
This paper introduces D-SYNC, a cutting-edge framework for enhancing the security and reliability of vehicle localization systems. D-SYNC offers an innovative solution to utilize dashcam footage for vehicle localization in the scenario of the primary localization system, GPS, under security attacks or simply failing to work. D-SYNC synchronizes the visual data from dashcam recordings with geotagged satellite imagery, achieving reliable vehicle positioning and trajectory mapping without precise dashcam-to-satellite alignment. D-SYNC introduces novel designs to address critical challenges, such as the limited field of vision in dashcam videos during real-world driving and how to correlate the significantly distinct spatial and temporal patterns between dashcam sequences and satellite images. Moreover, a novel geo-assisted loss function is introduced in D-SYNC that further elevates the performance of the localization process. D-SYNC surpasses existing methods and significantly increases vehicle localization accuracy. It achieves a 91% top-1 retrieval accuracy in urban environments and a 34% improvement in sub-10 meter localization error compared to benchmarks, relying solely on dashcam and satellite imagery.
| Original language | English (US) |
|---|---|
| Journal | Proceedings of the IEEE International Conference on Pervasive Computing and Communications, PerCom |
| Issue number | 2026 |
| DOIs | |
| State | Published - 2026 |
| Event | 24th IEEE International Conference on Pervasive Computing and Communications, PerCom 2026 - Pisa, Italy Duration: Mar 16 2026 → Mar 20 2026 |
Keywords
- Cross-view Image Matching
- Cyber-Resilient Vehicle Localization
- Dashcam-Satellite Fusion
- Image Retrieval
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Software
- Theoretical Computer Science
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