A Collaborative V2X Data Correction Method for Road Safety

• Published in: IEEE 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
• Language: English; 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
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2022.3159664

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.