Enhancing Vehicle Terminal Positioning Accuracy Using Neural Network and Lagged Variable Regression

The existing onboard positioning terminals face challenges of significant positioning errors and slow update speeds. This paper analyzes the factors influencing these errors and proposes a method for correcting vehicle terminal positioning errors using a BP neural network and lagged variable regression. Comparative analysis of three measurement data sets before and after correction shows maximum positioning error reductions of 88.2%, 85.4%, and 85.8%, respectively. Additionally, the comparison of pre- and post-correction positioning errors with measured data validates the effectiveness of the developed model utilizing BP neural networks and lagged variable regression for positioning error correction.