# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 5 Conclusions  
This paper proposes a prediction model of vehicle position change in the event of a GNSS outage based on position update architecture using LightGBM. The model solves the problem of data accuracy degradation of the GNSS/INS integrated navigation system for a vehicle when the satellite signal quality is weak or there is a GNSS outage and improves the existing AI-based GNSS/INS integrated navigation algorithm. When the vehicle is capable of receiving available GNSS signals, on-board INS and GNSS data are collected, and the PUA-LightGBM model is trained. In the event of a GNSS outage, INS data are used as the input to PUA-LightGBM to obtain the changes in longitude and latitude between the two epochs output by the model, thus predicting the vehicle's position at this time. Experiments on six road segments were conducted using a self-designed vehicle navigation data acquisition system. The results show that the error in predicting vehicle position in the event of a GNSS outage for PUA-LightGBM is less than that of PUA-RandomForest. However, the time required to train the PUA-LightGBM model is only 3.076 s, which is significantly less than the time required to train the PUA-RandomForest model. The authors believe that two factors should be considered in future research to enhance the predictive model's effectiveness in the event of a GNSS outage. One is reducing the noise in INS data to obtain better position predictions. The other is the higher training efficiency of PUA-LightGBM, which can save time and enable the model to be dynamically trained or updated during vehicle operation. Thus, the PUA-LightGBM can adapt to the prediction of vehicle position change on various complex road segments. The limitation of this study is that the validation of the PUA-LightGBM model was not real-time. It might be better to develop a real vehicle navigation system for embedding the PUA-LightGBM model in the future.  
[PERSON] and [PERSON] designed and performed the experiments and wrote the manuscript. [PERSON], [PERSON] and [PERSON]. ([PERSON]) supervised the work. [PERSON]. ([PERSON]) and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (62141303), The Science and Technology Guidance Plan of Gansu Province (2020-61-14) and the Gansu Provincial Science and Technology Major Project of China (21 ZD4 WA018).  
All datasets generated for this study are included in the article.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Outage Road** \\ **Segment** \\ \end{tabular} } & \multirow{2}{*}{**Road Length (m)**} & \multicolumn{2}{c}{**Average Absolute Positional Error (m)**} & \multicolumn{2}{c}{**Percentage**} \\ \cline{3-5}  & & **PUA-RandomForest** & **PUA-LightGBM** & **Improvement (\%)** \\ \hline a & 191.18 & 61.85 & 59.71 & +3.5 \\ b & 222.58 & 22.11 & 21.90 & +0.95 \\ c & 215.24 & 17.43 & 17.55 & \(-0.69\) \\ d & 264.46 & 32.84 & 33.52 & \(-2.1\) \\ e & 231.65 & 23.33 & 24.48 & \(-4.9\) \\ f & 146.88 & 22.03 & 21.92 & +0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average absolute positional error for the PUA-LightGBM model and the PUA-RandomForest model.