# 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
However, one limitation of this method is that it does not consider other road users or obstacles, which can pose significant challenges for autonomous vehicles in real-world driving scenarios. Three major architectural parameters of CNN were evaluated and compared in [45] to demonstrate their impact on the overall performance of the network. An optimal design of deep networks and the best performance of the network were achieved based on thiscomparison. The performance of CNN models was further enhanced by a new MSE-based ensemble approach, which was used for regression problems. A bagging method was utilized to demonstrate the superior performance of this new approach. A vision-based lateral control approach for autonomous driving using reinforcement learning and deep learning methods was proposed in [46]. The approach consists of a perception module and a control module. The perception module takes a driver-view image as input and predicts track features using a neural network of multi-task learning. The control module generates control decisions based on these features using reinforcement learning. The approach was evaluated using Visual TORCS (VTORCS) and a deep reinforcement learning environment based on the open racing car simulator (TORCS) to improve data efficiency. The trained reinforcement learning controller outperformed the model predictive control (MPC) and linear quadratic regulator (LQR) controllers on various tracks. The experiments showed that the perception module performs well, and the controller can effectively steer the vehicle along the center of the track using visual input. A data-driven simulator and an engine for training are proposed in [47] to learn control policies for end-to-end autonomous vehicles using sparse rewards. The simulator allows the policies learned to be generalized for navigating previously unseen real-world roads without the need for human-labeled training data. The effectiveness of the learned policies was demonstrated through experiments conducted on a full-scale autonomous vehicle in complex and novel scenarios, such as near-crash situations and new roads. The proposed approach leverages scalable reinforcement learning techniques and can be applied to achieve robust operation and efficient perception of autonomous vehicles in the physical world. Additionally, the approach enables autonomous vehicles to detect and avoid obstacles, including pedestrians, other vehicles, and trees, on the road. In [48], a deep Monte Carlo tree search (MCTS) algorithm based on reinforcement learning for vision-based control of autonomous driving was introduced. The algorithm uses driver-view images as input, captured by a camera mounted on the vehicle, and does not require human knowledge for vehicle control. The deep-MCTS algorithm performed virtual simulations of driving to predict maneuvers and enhance the stability of driving trajectory and steering control. Compared to current approaches, the proposed technique shows a significant improvement in the training efficiency, stability of driving trajectory, and stability of steering control, with 50.0%, 59.06%, and 66.30% improvement, respectively. The technique has the potential to be applied in real-life scenarios. A reinforcement learning (RL) approach for training a vehicle agent to perform automated lane changes in various scenarios was proposed in [49]. The RL algorithm uses a Q-function approximator to compute a closed-form greedy policy, enabling efficient computation of deep Q-learning with continuous action and state spaces. The simulation results demonstrated the effectiveness of the vehicle agent in learning efficient and smooth driving policies for lane changing. Furthermore, the adaptability and robustness of the RL agent were improved by testing in different traffic flow conditions and road geometries, making it suitable for complex driving scenarios. A novel model-free adaptive control (MFAC) algorithm called the dual successive projection (DuSP)-MFAC method was proposed and analyzed in [50].