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A Collaborative V2X Data Correction Method for Road Safety
Abstract
Driving safety is one of the most important points to concern on the road. Vehicles constantly generate messages under vehicle-to-everything (V2X) assisted driving. Especially, in dense urban environments, the massive messages carrying precise data can help us to improve road safety. However, vehicles do not always provide accurate data due to a variety of reasons, such as defective vehicle sensors, or selfish. It is critical to check and analyze the data supplied by vehicles in real time and correct the possible errors to eliminate the unsafe issues. In this article, we introduce a cOllaborative vehiClE dAta correctioN method (OCEAN) based on rationality and$Q$-learning techniques to correct the error V2X data for ensuring the driving safety of vehicles on the road, which can be deployed on both vehicles and road side unit. Extensive experimental results show that OCEAN can detect error V2X data up to 80$\%$and cut down 60$\%$average error distance for most attributes in vehicle data.
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“Several other V2X services have varying latency, reliability, and bandwidth requirements.”
From Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions (2024) · cites this paper as [132]
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Metadata
- Authors
- Zhao, L., Chai, H., Han, Y., Yu, K., & Mumtaz, S.
- Affiliations
- Shenyang Aerospace University (CN)
- School of Computer Science, Shenyang Aerospace University, Shenyang, China
- Waseda University (JP)
- University of Aveiro (PT)
- Venue
- IEEE Transactions on Reliability
- Published
- 2022-06
- Volume
- 71
- Issue
- 2
- Pages
- 951-962
- ISSN
- 0018-9529 (ISSN-L)
- 1558-1721
- OpenAlex
- W4225976909
- Cited by
- 110
- References
- 42
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 22 · 2023: 34 · 2022: 37
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Reliability (publishedVersion)
- Funders
- Department of Science and Technology of Liaoning Province
- Science Foundation of Liaoning Province (2020-MS-237)
- Fujian Normal University (202001)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Automotive Engineering › IoT and GPS-based Vehicle Safety Systems
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- vehicle-to-everything communication, error detection, road safety, Q-learning, rationality, roadside units, dense urban areas
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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