# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 1 Introduction  
Autonomous vehicles (AVs) have garnered increasing interest in recent decades due to advancements in computing devices and sensor technology, leading to numerous research programs involving the car manufacturing industry and academic laboratories [1]. The objective of this paper is to survey the current state-of-the-art on recent control methodologies applied to AVs. We start by presenting lateral, longitudinal, as well as the integrated lateral and longitudinal control of AVs. These methodologies form a base for the surveyed recent motion-control algorithms, and both the model-based control techniques as well as model-free control techniques are investigated. The self-driving concept has various socioeconomic benefits and impacts public transportation systems, including reducing accidents, improving passenger comfort, and optimizing fuel consumption [2, 3]. According to the World Health Organization (WHO), approximately 1.35 million people die each year from traffic-related accidents or injuries [4]. Adopting technologies such as advanced driver-assistance systems (ADAS) and automated driving systems (ADS) can significantly reduce over 80% of accidents caused by human error [4]. AVs can also reduce travel time, positively impacting passengers' mental and physical health, and provide economic benefits. They are also a viable mobility option for elderly and disabled individuals. InAustralia, for example, 79% of 9.2 million daily commuters spend an average of 25 min commuting to work [5]. Table 1 shows the list of acronyms and their definition. However, AVs present several social and technological challenges encountered in passenger and vehicle safety, efficient maneuvering on different terrains and environments, and fuel efficiency [6]. The complexity of ADS depends on the required level of autonomy, which consists of six levels (levels 0-5) ranging from \"no autonomy\" to \"fully autonomous\", according to the Society of Automotive Engineers' standard (SAE-J3016) [7]. The level of autonomy increases with the human subject's responsibility in the driving task, the complexity of driving assistance systems, and the operating conditions of the vehicles. The comparison presented in this survey helps provide insight into the strengths and limitations of recent control approaches for autonomous driving to tackle these challenges and assist with design choices. This paper focuses on the advanced control methodologies applicable to AVs that ensure the stability and safety of the vehicle system. Section 2 presents lateral, longitudinal, and integrated lateral and longitudinal control of AVs, while Section 3 discusses the control techniques used in autonomous vehicles. Finally, Section 4 offers concluding remarks and prospects for future research. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Acronyms** & **Definition** \\ \hline AVs & Autonomous vehicles \\ \hline RNNs & Recurrent neural networks \\ \hline DN & Deep network \\ \hline EN & Evolutionary network \\ \hline MPC & Model predictive control \\ \hline ML & Machine learning \\ \hline RL & Reinforcement learning \\ \hline WHO & World Health Organization \\ \hline RADAR & Radio detection and ranging \\ \hline DNN & Deep neural networks \\ \hline DDPG & Deep deterministic policy gradient \\ \hline MCTS & Monte Carlo tree search \\ \hline DQN & Deep Q network \\ \hline TORCS & The open racing car simulator \\ \hline CNNs & Convolutional neural network \\ \hline LSTM & Long short-term memory \\ \hline AI & Artificial intelligence \\ \hline DL & Deep learning \\ \hline LIDAR & Light detection and ranging \\ \hline MFCN & Motion-aid feature calibration network \\ \hline NN & Neural networks \\ \hline MDP & Markov decision process \\ \hline DVSL & Differential variable speed limit \\ \hline DoF & Degrees of freedom \\ \hline GAN & Generative adversarial network \\ \hline PID & Proportional integral derivative \\ \hline OEM & Original equipment manufacturer \\ \hline PPC & Pure pursuit controller \\ \hline \hline \end{tabular}
\end{table}