# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Longitudinal Control of Autonomous Vehicles  
#### 2.2.1 Longitudinal Model-Based Control Techniques  
To ensure proper longitudinal control, four types of information are required: the speed and acceleration of the ego vehicle, the speed and acceleration of the preceding vehicle, the distance to the preceding vehicle, and the speed and acceleration of the leader vehicle in a platoon scenario. The OEM speed sensors and accelerometers can measure the speed and acceleration of the ego vehicle, as shown in Figure 3. Range sensors such as vision, ultrasonic, LIDAR, and radar can measure the distance to the preceding vehicle, with radar being the most common sensor used. The physical principle of automotive radar is the main reason for its success and provides unique characteristics of performance at a reasonable cost. These characteristics include design compatibility, vehicle integration independent of environmental conditions (weather and light), multiple fields-of-view capability, Doppler velocity, and directly measured spatial parameters. Radar works in case of the failure of other sensors. This radar can virtually observe the perspective effect of vehicles by utilizing the reflection between the vehicle floor and the road surface to make invisible objects visible. Mutual interference between vehicle radars represents a challenging problem that must be considered. For example, the consequences can be unimaginable, and the radar can misjudge the surrounding environment when the speed is high, when two autonomous vehicles are driving beside each other, and when the radars affect each other [54]. The speed and acceleration of the preceding vehicle can be measured in two ways: by using range sensors and deriving the information from the ego vehicle or by communicating the information between the vehicles. In a platoon scenario, the lead vehicle's speed and acceleration can be obtained through communication. However, the reliability of communication cannot be fully trusted. The autonomous vehicle Leonie utilizes the adaptive longitudinal control system described as [55]. This system was designed to respond to changing weather and road conditions through a calculation known as the grip value. The grip value influences several key factors, including the safe time headway, the magnitude of acceleration demands, and the maximum allowable vehicle speed for both the longitudinal controller and the entire vehicle-guidance system. By incorporating the grip value calculation, the results of test drives demonstrated a smoother acceleration profile and reduced interference from electronic stability control (ESC) and automatic traction control (ATC) systems. In [56], a comparison was made between four different longitudinal controllers. The first was a classical proportional-integral (PI) control, which is a well-established control method. The second was an advanced i-PI control, which incorporates an intelligent component into the traditional PI control. The third was a fuzzy control system based on human experience, and the fourth was a neuro-fuzzy control system. The aim of the comparison was to evaluate the performance of these controllers under various driving conditions and identify the most suitable control method for autonomous vehicles. The proposed control techniques were validated by numerical simulations in first step, and experiment results were highlighted on a real vehicle in the second one. A longitudinal controller for an automated driving bus was developed in [57]. A linear longitudinal model was presented by approximating a rolling resistance force and an aerodynamic drag force in a low speed range. Then, the feedback gains of a proportional integral (PI) control were determined using a root locus method to consider the longitudinal grade of the road with a constraint derived from the Chien-Hrones-Reswick (CHR) method. The vehicle predicts the traffic light for the smooth passing of a signalized intersection.