The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
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]
Citations
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Novel abnormal data correction method based on GA-RBF neural network considering power balance
Li, X., Yan, S., He, H., Lv, L., Cai, M., Cui, J., & Li, C. (2024) · International Conference on Energy System, Electricity, and Power (ESEP) · doi:10.1117/12.3024509
Real-Time Anomaly Traffic Data Identification Method for Connected Vehicles in V2X Communication
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Automotive Information Security Assessment and Risk Management Methods
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Safe Intelligent Vehicles: Intent Is What We Need
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A tarp and ensemble classifier-based real-time congestion control approach for V2V communication
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Misbehavior Detection in Internet of Vehicles: A Multi-Dimensional SurveyPreprint
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A Probabilistic Collision Risk Framework for Two-Lane Roads in V2X-Enabled Networks
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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
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