# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 4 Concluding Remarks and Future Works  
This paper proposes a review on recent advanced control methodologies applied for autonomous vehicles (AVs), which can be generally classified into lateral, longitudinal, and integrated lateral and longitudinal model-based and model-free control schemes. Because the development of AVs is driven by recent advances in methodologies of vehicle control, intelligent transportation systems, artificial intelligence (AI) techniques such as deep learning (DL) and reinforcement learning (RL), and computational systems, this progress has led to intelligent vehicles, smart roads, intelligent traffic safety, and improved passenger comfort.  
Current research work is focused on handling the main issues related to AVs, such as model-based and model-free control techniques. Model-based control approaches are accurate and efficient for an accurate system model but are limited by model uncertainties. Model-free control approaches are robust against model uncertainties but need more data and time to learn the control policy, which represent the control issues of intelligent connected vehicles developed on the basis of intelligent vehicles. The choice between these two approaches relies on the experience of the control system designer, available data and computational resources, and specific requirements of the control problem. Autonomous vehicles use both model-based and model-free control techniques to obtain high performance and robustness. AVs can take advantage of the strengths of both control types and alleviate their weaknesses using these approaches.  
The potential of recent control techniques in various fields of AVs is highlighted, with a focus on the objectives that can be achieved in these aspects. The improvements achieved by advanced control techniques, which overcome the limitations of traditional ones in autonomous driving, are also demonstrated.  
Finally, the main existing research challenges are addressed, and future research areas in the direction of fully autonomous/driverless vehicles is identified as a vision and suggestions complementary to this work through the integration of different advanced control methodologies, with different vehicle models improving their performance under various assumptions in various driving scenarios and regions in different research areas. The literature survey presented in this paper will support future research initiatives as a guideline in autonomous vehicles.  
**Author Contributions:** Conceptualization, H.R. and A.C.; Formal analysis, H.R. and A.C.; Investigation, H.R. and A.C.; Methodology, H.R., A.C. and A.K.; Supervision, A.C. and A.K., Writing--original draft, H.R., A.C. and A.K.; Writing--review & editing, H.R., A.C. and A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded and supported by French Ministry of Higher Education and Research.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares there is no conflict of interest.