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, 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.

Automated summary

This article presents a collaborative vehicle data correction method for correcting V2X data errors to enhance driving safety. Experimental results demonstrate the effectiveness of the method in detecting and correcting erroneous data.