# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Lateral Control of Autonomous Vehicles
#### 2.1.2 Lateral Model-Free Control techniques  
There are complex road situations such as crosswalks, dense traffic, intersections, etc. Due to these reasons, artificial intelligence (AI) techniques, reinforcement learning (RL), and deep learning (DL) learn from their environment to outperform the model-based control techniques to take accurate control command, such as steering [38]. A recent study proposed an end-to-end control system for steering autonomous vehicles using a convolutional neural network (CNN), referred to in [39]. The system does not rely on explicit hand-engineered algorithms for path planning, lane detection, or object detection and instead maps pixel data directly to steering commands using the trained neural net. This means that any other sensors are not required for the system to function. The performance of the controller was evaluated by comparing its steering behavior with that of a human driver. In [40], the study detailed the training of an end-to-end model for controlling the lateral motion of autonomous vehicles (AVs). The authors demonstrated that CNNs can drive in similar but previously unseen scenarios. To train the network, a small amount of training data was used within a simulation to control the car. Furthermore, the study suggests that this approach can be extended to integrate longitudinal control of AVs by training the network to use both steering angles and vehicle speed to achieve the desired steering angles and vehicle speed. An end-to-end autonomous driving system that combines long short-term memory (LSTM) and CNN architectures was proposed in [41]. The integration of LSTM and CNN allowed the system to extract both spatial and temporal features, and changes in the steering-wheel angle of the vehicle were considered over time. However, data collection from real-world vehicles was limited due to the challenges in processing vehicle data. To overcome this issue, the authors used a driving simulator to train the proposed approach for the steering wheel angle. The study suggests that a complete end-to-end driving system could be constructed using large-scale vehicle data, provided solutions to the limitations in vehicle data collection are found. In [42], a deep neural network technique was proposed to explore the potential of event cameras in predicting the steering angle of a vehicle, which is a challenging task in motion estimation. The performance of this approach was evaluated on a publicly available dataset of large-scale event-camera data. The simulation results demonstrated that the proposed approach performs well using event cameras under different conditions, including challenging illumination and fast motion, compared to traditional standard cameras. In [43], a novel network architecture was developed to enhance the end-to-end learning of the steering angle based on baseline vision using auxiliary tasks. The architecture incorporated vehicle kinematics, recurrence modules, segmentation of auxiliary image, introduction of optical flow, and transfer from existing tasks as distinct auxiliary tasks. The Imagenet recognition task was used to learn features that were useful for on-road driving in the steering task. By enhancing the mask of the pre-trained segmentation with more information, a more accurate prediction could be obtained. Additionally, the precise addition of vehicle kinematics led to a more concrete representation of the state and boosted performance. The proposed approach was evaluated using the Comma.ai dataset and Udacity simulator, demonstrating its effectiveness in improving end-to-end learning of the steering angle. An advanced Chauffeur hybrid method was utilized to steer autonomous vehicles in various situations, including changing from one road to another, lane changes, traffic signs, and traffic lights [44]. CNNs were used to extract data features from the Udacity simulator, and both recurrent neural networks (RNNs) and CNNs were employed.