# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 8 Future Work and Limitations
### Future Work  
1. Integration with V2X Technologies: Future research will focus on leveraging vehicle-to-everything (V2X) communication to enable dynamic curvature forecasting. By incorporating real-time data from connected vehicles and infrastructure, the controller can proactively adjust parameters, enhancing tracking accuracy and stability in anticipation of upcoming road conditions.
2. Enhanced Real-Time Adaptability: Exploring machine-learning techniques, such as online learning or neural networks, could further optimize the controller's adaptability. These methods could enable the system to learn from real-time driving data and continuously refine its control strategies.
3. Expanded Hardware-in-the-Loop (HIL) Testing: Future work will include more extensive HIL testing under a wider range of scenarios, such as mixed urban and highway environments, to validate the controller's performance in diverse operational conditions.
4. Multi-Objective Optimization: The current controller prioritizes path tracking accuracy and stability. Future iterations could incorporate additional objectives, such as energy efficiency or passenger comfort, into the optimization framework to achieve a more holistic control strategy.
5. Generalization to Other Vehicle Types: While the current study focuses on intelligent passenger vehicles, the proposed methodology could be extended to other vehicle types, such as commercial trucks or agricultural machinery, by adapting the dynamic model and constraints accordingly.  
**Author Contributions:** [PERSON] and [PERSON] designed the scheme; [PERSON] checked the feasibility of the scheme; [PERSON] designed the control model and performed the software simulation experiments; [PERSON] and [PERSON] jointly designed and developed the controller hardware; [PERSON] provided the resources; [PERSON] assisted [PERSON] in conducting the real vehicle test; [PERSON] wrote the paper with the help of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 16 KJA580001 and the National Natural Science Foundation of China under Grant 51675235.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in insert article.  
**Acknowledgments:** The authors would like to express their sincere gratitude to the anonymous reviewers for their valuable comments and suggestions, which significantly improved the quality of this paper. Special thanks are also extended to the research teams at Jiangsu University for their technical support and constructive discussions during the development of this study. Additionally, the authors acknowledge the contributions of CarSim and MATLAB/Simulink for providing the simulation tools that facilitated this research. Finally, we appreciate the efforts of all colleagues and students who contributed to the experiments and data analysis.  
**Conflicts of Interest:** The authors declare no conflicts of interest.