# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 1 Introduction  
The current huge jump in self-driving stems from evolutionary development since the birth of the autonomous-driving concept. This concept is based not only on the substitution of the human driver but also a reduction in fatal accidents due to human factors. Indeed, carmakers search to meet the challenge by designing sophisticated and fully automated guidance systems. The development of an autonomous vehicle involves three key phases: environment perception, path planning, and path tracking [1]. The first phase permits one to perceive the vehicle's external surroundings; the second phase exploits the data issued from the first phase, while the third phase is charged with decision-making. In other words, it tries to impose on the vehicle dynamics the path arising from the second phase. The third phase focuses on the control systems, which are in charge of tracking the target path and enforcing it on the vehicle. The fundamental mission of the control unit is to maintain the vehicle's dynamic stability. This unit has two layers; the first is dedicated to the control algorithm, and the second is dedicated to the actuators. The latter is responsible for the execution of the orders issued by the first layer. These orders are algorithms that concern lateral and longitudinal control [2]. The concepts and physics of longitudinal vehicle control strategies have been studied since the late 1960s. Various longitudinal control approaches have been elaborated [3]. Longitudinal control can be characterized as the system of control strategies that are applied to control the behavior of a vehicle on its longitudinal trajectory via the various actuators assigned to acceleration and braking [4]. These longitudinal control techniques target advanced driver assistance system (ADAS) features. Adaptive cruise control (ACC) andintelligent speed assistance (ISA) are ADAS features that are relevant to longitudinal control. As mentioned earlier, the nucleus of the control is either based on conventional control theory approaches, strategies based on automatic learning theory, or a combination of both. Autonomous ground vehicles (AGV) longitudinal dynamics are a long-standing challenge in the automotive area. A variety of control methods are offered; the sliding mode control (SMC) is recognized as the dominant approach to controlling the dynamics of autonomous systems in general [5; 6; 7; 8] and AGVs in particular [1; 9; 10]. In this light, the authors of [11] offered a second-order sliding mode controller. This technique has been used to conceive a longitudinal controller that can adjust the inter-obstacle distance. Experimental tests have been conducted to verify the effectiveness of this approach. This study addressed the improvement of the ACC and stop-and-go features of ADASs. In the same vein, the authors of [12] intended to provide the best control performances of the ACC feature based on an optimal super-twisting sliding mode control design. In a similar fashion, sliding mode control combined with a fuzzy control technique was elaborated in [13]. The SMC drove the vehicle from a random primary position to a potential parking place. At the same time, the fuzzy controller took over to park the vehicle in this desired position. This strategy was designed to improve autonomous parking algorithms. A backstepping controller merged with an extended Luenberger observer was discussed in [14]. Based on a quarter-vehicle model, the states observer provided online estimations of the brake torque, angular wheel speed, and vehicle speed. This design was intended to enhance the performance of the antilock braking system (ABS). An MPC tracking algorithm incorporating longitudinal velocity compensation to minimize tracking error and ensure vehicle stability was discussed in [15]. This method managed the lateral and longitudinal dynamics concurrently to achieve the vehicle's complete stability. Over and above conventional proposals, combinations of deterministic and intelligent methods are omnipresent in the literature.