A Robust Localization Algorithm for Intelligent and Connected Vehicles by Variational Bayesian Method

Vehicle localization is a basic and important part of intelligent and connected vehicle technology. However, intelligent and connected vehicles may be affected by outliers when they are driving in complex environments. Additionally, there may be random packet dropouts and delays when positioning data is transmitted by vehicle-to-vehicle communication technology. These factors can cause intelligent and connected vehicles to be unable to obtain real-time and accurate position information, which poses security risks to intelligent transportation. To ensure accurate positioning of intelligent and connected vehicles, this paper takes into account factors such as packet dropouts, delays, and outliers during data transmission. The measurement packet dropouts and randomly delayed data transmissions are described by two Bernoulli variables, while the outliers are modelled as a student’s t distribution. As a result, a robust state-space model is developed, and the unknown states and parameters of the proposed model are calculated by the variational Bayesian method. Finally, the experimental results indicate the effectiveness.