# 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  
By employing both lateral and longitudinal controls in conjunction, we can enhance both acceleration and steering performance. To this end, various artificial intelligence (AI) techniques have been developed that allow the estimation of control commands (e.g., steering, braking, and acceleration) in real time. These approaches leverage the power of AI to optimize vehicle handling and responsiveness, leading to safer and more efficient driving experiences. In [84], a multi-task learning framework was proposed in an end-to-end manner to simultaneously predict speed control and steering angle. The network utilized image sequences to predict steering angles and discrete commands of speed because visual inputs alone were not sufficient to predict accurate speed. To address this, a multi-modal multi-task network was used, which incorporated both visual recordings and previous feedback speeds as inputs to predict steering angles and speed values. The proposed approach was evaluated on a newly collected SAIC dataset and a public Udacity dataset, and the results demonstrated the accuracy of speed value and steering angle predictions. The problem of error accumulation was addressed using methods of failure data synthesis in real road tests. An investigation of the effect of connected and autonomous vehicles (CAVs) on traffic flow in mixed traffic scenarios, with a focus on on-ramp merging and off-ramp diverging of vehicles, was presented in [85]. The study explored the impact of increasing market-penetration rates of CAVs and proposed a lane-changing cooperative strategy based on reinforcement learning to enable CAVs to make farsighted lane changes and improve traffic efficiency. Simulation results showed that incorporating CAVs can improve traffic capacity, flow, and mean speed, with significant impacts on the processes of lane changes at on/off-ramps. This study highlighted the mixed dynamics of traffic networks and the potential of CAVs for future mobility. In [86], a new approach was developed, referred to as \"implicit affordances\", for applying reinforcement learning to urban driving tasks such as vehicle and pedestrian avoidance, traffic light detection, and lane keeping. Ablation studies were conducted to confirm the design choices, and the proposed method demonstrated high performance by winning the camera only track in the CARLA challenge. In [87], a dynamic obstacle avoidance model predictive control (MPC) framework was proposed for autonomous driving, which incorporates deep learning techniques to achieve velocity-based collision avoidance in unknown environments. The main goal of this approach was to enable the autonomous vehicle to perform various safe traffic maneuvers in the shortest possible time and with maximum passenger comfort while considering the vehicle's dynamics and maneuvering capabilities as well as road boundaries, traffic rules, and static/dynamic unknown obstacles. To address the dynamic collision avoidance problem, the authors defined local coordinates and collision regions, which allowed them to convert it into a static collision avoidance problem. This conversion simplifies the optimization process, and the authors used an ensemble of deep neural networks to estimate collision probabilities and prioritize between safety and mission using an uncertainty-based collision cost. The MPC generates a predicted trajectory by leveraging a learning procedure that predicts collisions in advance using labeled data. The proposed method was tested in different simulation environments and scenarios, and the results demonstrated its safety, good performance, and adaptability to unknown environments. In [88], an overview of deep learning techniques applied in the context of autonomous driving was presented. The survey covered recurrent and convolutional neural networks, deep reinforcement learning, and AI-based self-driving architectures.