# 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  
#### 2.3.1 Integrated Lateral and Longitudinal Model-Based Control Techniques  
Great attention towards the autonomous vehicles' development has appeared in the last decades in the academic, research, military, and industry fields. The guarantee of secure and reliable navigation of vehicles even in critical situations of driving is the main motivation. The perception and localization, the planning of trajectory, and the control of the vehicle are the three main steps to achieve vehicle autonomy. Lateral control [37,73] and longitudinal control [74,75] for the dynamics of vehicles are separately addressed in most control techniques in the literature, and the control of longitudinal and lateral vehicle dynamics could be separated or coupled, as shown in Figure 4, depending on the control methodology, the dynamics model used, the assumptions, considered uncertainties and parameter variation, and the applications for both dynamics. The coupling of vehicle dynamics must be considered in the vehicle control design for the vehicle control and to handle its stability and safety, as shown in Figure 5. The lateral and longitudinal control problem of vehicle dynamics was recently addressed in a coupled way by some research groups. Control algorithms for autonomous vehicles operating at tire adhesion limits are presented in [76]. The algorithms include feedforward and feedback components that replicate the actions of a skilled racecar driver in managing throttle, brakes, and steering. In terms of ensuring safe and efficient vehicle operation at the limits, the feedback control, specifically heading error feedback, surpassed lookahead error feedback. This approach minimizes tracking errors, enhances stability, and guarantees precise trajectory following. In [77], a nonlinear cascade control system was developed for longitudinal control, ensuring torque control and reference speed tracking. The longitudinal control is interconnected with a model predictive control (MPC)-based lateral control for full autonomous driving. Simulations demonstrated the effectiveness of the longitudinal controller in tracking time-varying reference speeds. Real-world trajectory data validated the promising performance of the automated guidance strategy. Figure 4: Block diagram of integrated longitudinal and lateral control. Figure 5: Deep-learning-based autonomous vehicle. A method for platoon control was proposed in [78], where longitudinal and lateral control were combined to ensure the vehicle remains within a lane. The integration of frequency and time domain methods in the longitudinal control allows for consideration of safety, performance, comfort, and actuator limitations, thereby ensuring string stability. The lateral control, employing LPV-MPC and convex optimization, was utilized to address speed changes resulting from longitudinal control and attain global optimality. With this method, the vehicle is maintained within the lane with minimal tracking errors, even when longitudinal velocities vary. The control strategy for fully automated guidance in an automotive vehicle, with a focus on longitudinal and lateral control, was discussed in [79]. Simultaneous utilization of both control types effectively handles the vehicle's complex dynamics. Nonlinear model predictive control (NLMPC) achieves steering automation, while a nonlinear longitudinal control strategy addresses speed tracking and powertrain dynamics. Simulations validated the approach, demonstrating efficient path tracking, acceptable lateral errors, and accurate longitudinal speed tracking. Overcoming the challenge of strong coupling between lateral and longitudinal dynamics in the design of robust, real-time controllers for real vehicle implementation remains a key consideration. In [80], two coupled controllers were proposed: one utilizing Lyapunov control techniques and the other employing immersion and invariance with a sliding mode approach.