A Collaborative V2X Data Correction Method for Road Safety

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, vehicl...

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Bibliographic Details
Published inIEEE Transactions on Reliability Vol. 71; no. 2; pp. 951 - 962
Main Authors Zhao, Liang, Chai, Hongmei, Han, Yuan, Yu, Keping, Mumtaz, Shahid
Format Journal Article
LanguageEnglish
Japanese
Published New York IEEE 01.06.2022
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Basic safety message (BSM)
Collaboration
data correction
Error correction
Error detection
Messages
Real-time systems
Road safety
Roadsides
Safety
Sensors
software-defined vehicular network
Traffic accidents & safety
Traffic safety
Training
Urban areas
Urban environments
VANET
Vehicle safety
Vehicle-to-everything
vehicle-to-everything (V2X)
Vehicles
Online AccessGet full text
ISSN0018-9529
1558-1721
DOI10.1109/TR.2022.3159664

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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 <inline-formula><tex-math notation="LaTeX">Q</tex-math></inline-formula>-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<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> and cut down 60<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> average error distance for most attributes in vehicle data.
AbstractList 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 <inline-formula><tex-math notation="LaTeX">Q</tex-math></inline-formula>-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<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> and cut down 60<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> average error distance for most attributes in vehicle data.
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 [Formula Omitted]-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[Formula Omitted] and cut down 60[Formula Omitted] average error distance for most attributes in vehicle data.
Author Mumtaz, Shahid
Han, Yuan
Yu, Keping
Chai, Hongmei
Zhao, Liang
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  organization: Instituto de Telecomunicações and Universidade de Aveiro, Aveiro, Portugal
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Snippet Driving safety is one of the most important points to concern on the road. Vehicles constantly generate messages under vehicle-to-everything (V2X) assisted...
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SubjectTerms Basic safety message (BSM)
Collaboration
data correction
Error correction
Error detection
Messages
Real-time systems
Road safety
Roadsides
Safety
Sensors
software-defined vehicular network
Traffic accidents & safety
Traffic safety
Training
Urban areas
Urban environments
VANET
Vehicle safety
Vehicle-to-everything
vehicle-to-everything (V2X)
Vehicles
Title A Collaborative V2X Data Correction Method for Road Safety
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