# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Integrated Lateral and Longitudinal Control of Autonomous Vehicles
#### 2.3.2 Integrated Lateral and Longitudinal Model-Free Control Techniques
These methodologies are crucial for driving scene perception, path planning, arbitration of behavior, and motion control algorithms. The article discussed both modular perception-planning-action pipelines and end-to-end systems that use deep learning methods to build each module. However, the current challenges in designing AI architectures for autonomous driving include ensuring safety, addressing computational hardware limitations, and obtaining adequate training data sources. The survey provided a comparison of the features andlimitations of AI and deep learning techniques in autonomous driving, which can assist in making informed design choices. A block diagram of the deep-learning-based autonomous vehicle is shown in Figure 5 [88] and the schematic diagram of a vehicle control system using RL is depicted in Figure 6.  
The complex and nonlinear system of an autonomous vehicle was successfully subjected to simultaneous longitudinal and lateral control [89]. This was achieved through a series of steps including selecting an appropriate adaptive neural network that can generate both linear and complex mappings, developing an adaptive lateral control, and evaluating its robustness and precision against parametric uncertainty. Additionally, an adaptive longitudinal control was developed, and its performance was compared with that of a sliding-mode-based longitudinal control and a simple neural-network-based longitudinal control. A simultaneous combined control was proposed for the longitudinal and lateral dynamics of the autonomous vehicle, which outperformed the other control approaches in achieving simultaneous speed-tracking and path-following objectives, as demonstrated through simulation results. In [90], lateral and longitudinal control of autonomous vehicles (AVs) motion was designed utilizing deep learning. The control implementation was developed and tested utilizing the open racing car simulator (TORCS). The vehicle steering and speed were predicted by training two separate neural networks based on the road trajectory. A system that uses artificial intelligence was built, using such an approach to determine the value of the vehicle speed rather than respecting a set of predetermined rules and analyzing the environment. The existing vehicle lateral and longitudinal control techniques using DL and RL are compared in Tables 5 and 6, respectively. Figure 6: Vehicle control system using RL. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Pappers** & **Publication** & **Year** & **Sensor Input** & **Dataset** & **Output** & **Nested Network Architecture** & **DL Framework** & **Hardware** & **Validation** & **Advantages** & **Disadvantages** \\ \hline
[44] & 2018 & Cameras & \begin{tabular}{c} Udacity and \\ SalC \\ \end{tabular}  & \ \begin{tabular}{c} Soreing \\ angle and \\ symbol \\ command \\ \end{tabular}  & CNN and LSTM & Not reported &
\begin{tabular}{c