## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing Prediction Model
#### 3.1.3 Prediction Model Pre-Training Based on Transfer Learning  
In ML, a large amount of data is often required to learn complex features. The process of acquiring the data for training the mobile platform prediction model is a laborious task. It is noteworthy that as soon as the feature space or feature distribution of the test data changes, the performance of model degrades. As a result, new data must be collected to retrain the model for enhancing the performance.  
The transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate the predictive modeling consisting of different data patterns in the current domain [94]. These methods include sample transfer, feature transfer, model transfer, and relation transfer. In natural language processing and computer vision, the  
Figure 4: The control framework and experimental terrain of RC-LB-NMPC method [32; 75; 92].  
pre-trained models created for a specific task are used to solve problems of a different domain [95]. However, there are not many neural networks available for path tracking control in mobile platforms. Therefore, the simulation dataset for model pre-training is obtained by building simulation models, such as dynamics. The data obtained from the real mobile platform is used to fine-tune the prediction model. As a result, the requirement for real data is reduced. The degree of similarity between the simulation and real data significantly influences the results of pre-training.  
In multibody dynamics simulation (MBS) analysis, there are three challenges, namely, high modeling difficulty, high computational complexity, and restricted solver. The MBS based on deep learning network (MBSNet) is applied to vehicle tracking systems [96]. This model is robust in the presence of different track irregularities. The MBSNet accurately and quickly predicts the low-frequency components of dynamic response. The technical flowchart of MBSNet is presented in Figure 5.  
The end-to-end training methods use the original sensor data as the input and train the output directly by commands. These methods decrease the volume of training data because they are only trained on real devices or simulations without experiments. The convolutional neural network (CNN) is used as an image embedder for estimating the current and future position of the vehicle relative to the path [97]. As presented in Figure 6, a region randomization method is proposed to generate different types of paths with random curvature and length, initial lateral, and heading errors for the vehicle during simulation. This method covers all the possible training scenarios. In addition, this method prevents the network from overfitting, which makes it more universal as compared to other similar methods. The mobile platforms follow the path smoothly and slow down where the curvature is larger.  
Figure 5: The technical flowchart of multi-body dynamics simulation based on deep learning network [96].  
In cases of uncertain disturbances, the data-driven prediction models have a better environmental adaptability as compared to the traditional physical models. The transfer learning is used to pre-train the prediction model in the simulation environment, which solves the problem of high demand for real training data. The transfer learning also improves the reliability of the data-driven prediction models, which is the basis of LB-MPC.