Cited by 111Vehicular Ad Hoc Networks (VANETs)IoT and GPS-based Vehicle Safety SystemsAutonomous Vehicle Technology and Safety

A Collaborative V2X Data Correction Method for Road Safety

Zhao, Liang, Chai, Hongmei, Han, Yuan, Yu, Keping, Mumtaz, Shahid·2022·IEEE Transactions on Reliability

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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Crossref · 10.1109/tr.2022.3159664

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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
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 › Mechanical 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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