# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 3 Discussion  
Model-based control techniques are as follows:  
* Require a mathematical model of the system being controlled;
* Use the model to predict the behavior of the system and optimize control inputs;
* Are often more efficient and accurate than model-free techniques when the model is accurate;
* May be limited by the accuracy and completeness of the model;
* Are typically designed by control engineers who have expertise in modeling and system identification.
* Model-free control techniques are as follows:
* Do not require an explicit model of the system being controlled;
* Learn control policies from data through trial and error;
* Can be used when the system is highly complex, poorly understood, or changing rapidly;
* Are often more robust to model uncertainties than model-based techniques;
* May require a large amount of data and time to learn a control policy;
* Are typically designed by machine learning experts who have expertise in reinforcement learning or other model-free techniques.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Papers** & **Publication** & **Contributions** & **Output** & **RL Technique** & **Scenarios** & **Validation** \\ \hline
[93] & 2017 & Modeling of driver and vehicle interactions using game theoretic and RL & Decelerate and hand telecarate and maintain & MDP & Multi-lane highways & Simulation \\ \hline
[94] & 2018 & Controllable imitative reinforcement learning to achieve higher success & Steering and brake and acceleration & DDPG & Urban traffic & Simulation \\ \hline
[95] & 2020 & RL model for differential variable speed limit control & Speed limits & DDPG & Freeway with five-lane & Simulation \\ \hline
[96] & 2020 & Model-based RL of the complex driving environment methodology & Steering and acceleration and brake & RNNs and EN and DN & Urban driving & Simulation \\ \hline
[97] & 2020 & Combination of RL and game theory to learn merging behaviors & Steering and velocity & DQN & Urban traffic & Simulation \\ \hline
[98] & 2020 & Automated lane-change strategy using proximal policy optimization-based RL & Lane change and acceleration & NN & Highways & Simulation and real \\ \hline \hline \end{tabular}
\end{table}
Table 6: Integrated lateral and longitudinal control techniques using RL.  
We suggest combining model-based and model-free techniques to leverage the strengths, avoid the limitations, improve the performance, and show the effectiveness of both approaches. For example, one approach to combine these techniques is to use reinforcement learning (RL) and deep learning (DL). RL is a type of model-free control that learns from experience to improve control decisions. RL can be used to learn a control policy that optimizes a performance metric, while a model-based controller can be used to provide stability guarantees and constraints. Another approach is to use a hybrid controller that switches between model-based and model-free models depending on the situation. For example, a model-based controller could be used when the system is in a known operating regime, while a model-free controller could be used when the system enters an unknown or highly dynamic regime. Model-based control techniques are commonly used in autonomous vehicles to model the dynamics of the vehicle and its environment and to design control laws that optimize safety, efficiency, and other performance criteria. For example, model-based controllers can be used to regulate the vehicle's speed, steering, and braking and to ensure that the vehicle stays within its lane and avoids collisions with other objects. However, model-based control techniques can be limited by the accuracy and completeness of the model and by the complexity of the system being controlled. This is where model-free control techniques can be useful, as they can learn control policies directly from data without requiring a model of the system.