# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 1 Introduction
[PERSON] and [PERSON] proposed a novel low-cost high-performance vehicle integrated navigation algorithm. It employed EKF to fuse data from GNSS, INS, visual odometry, and vehicle dynamics models (as virtual sensors) [9]. The other category is artificial intelligence (AI) technology-based integrated navigation algorithms for vehicles, which aim to find linear and non-linear relationships between sensor data during vehicle operation, thus completing the process of data fusion. [PERSON] et al. used multilayer feedforward neural networks and backpropagation learning algorithms to improve the INS/DGPS integrated navigation method for vehicles. The resulting algorithm eliminates the effects of neural network parameters and random noise on positioning accuracy [10]. [PERSON] et al. proposed a D-S neural network for GPS/INS data fusion by combining D-S evidence theory with a neural network model. The authors found that the model improved the positioning accuracy of the vehicle integrated navigation system under GPS outage conditions [11]. [PERSON] et al. implemented an autoregressive process in a stochastic modeling approach based on microelectromechanical systems inertial sensor (MEMS-INS) errors to reduce INS errors. The authors used AI to improve the accuracy of information fusion by enhancing the conventional KF module, resulting in improved performance of a low-cost MEMS-INS/GPS integrated navigation system for vehicles [12]. [PERSON] and [PERSON] proposed an INS/GPS integrated navigation method that combines artificial neural networks and uses historical information. This method prevents the continuous growth of INS errors when GPS is unavailable for long periods [13]. [PERSON] et al. proposed a two-layer, highly robust algorithm for vehicle integrated navigation systems that fuses GNSS and INS data via support vector machine regression (SVR) and adaptive Kalman filtering (AKF). This resulted in an algorithm that improves vehicle integrated navigation system positioning accuracy when GNSS signals are weak [14]. [PERSON] et al. proposed a GPS/INS neural network (GI-NN) deep learning network structure as an INS aid when GPS is unavailable; this algorithm uses historical data from the IMU and exploits both spatial and temporal features in the IMU to achieve better localization results [15]. Using a T2 FHNN (type-2 fuzzy Hammerstein neural network), [PERSON] et al. proposed a data fusion scheme for a SINS/GNSS integrated navigation system with better navigation accuracy and stability [16]. In summary, some researchers have made significant progress in improving vehicle positioning accuracy through improved integrated navigation algorithms. However, little information is available on vehicle integrated navigation algorithms based on efficient AI methods. The machine learning methods used in the existing literature for vehicle position prediction models almost did not consider the efficiency optimization issue, such as the cost of time and computational resources required for model training [12; 15]. In addition, most previous AI models for predicting a vehicle's position in the event of a GNSS outage cannot be updated online after the initial machine learning training. This results in these models only having good position prediction accuracy when the vehicle is driving on certain types of road segments covered in the training set. Moreover, the models cannot adapt to complex and dynamic changes in road segments. Overall, these models lack robustness. The AI method based on an improved position update architecture (PUA) for GNSS/INS integrated navigation can predict the position of a vehicle in the case of a GNSS outage. As a result, the objective of this study is to propose a new LightGBM machine learning algorithm based on an improved PUA for GNSS/INS integrated navigation that predicts vehicle position in the event of a GNSS outage. Additionally, the study proposes to solve the problem of the inefficient model training used in AI-based integrated navigation algorithms by improving model training efficiency.