# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 1 Introduction  
In the field of transportation, Intelligent Transport Systems (ITS) are seen as a guiding light for creativity, a guarantee that not only improved road safety but also enjoyable driving will be there. This technology centers on equipping road users with enough information to make informed decisions so as to prevent road accidents and enhance general road safety [1, 2]. Nevertheless, the World Health Organization (WHO) has a more horriifying revelation, with over 1.3 million deaths annually attributed to traffic due to both motorized and non-motorized users such as walkers, cyclists, and motorbisers [3]. The invention of AVs offers hope for safer roads all over the world. It represents a global solution to this concern. These companies aim to significantly reduce road accidents by applying technology that can understand and interpret the behavior of road users, therefore, protecting them on the road, especially pedestrians, who are considered the most Vulnerable Road Users (VRUs) [4, 5]. According to the transport revolution, AVs collaborate not only with each other but also with road infrastructure and VRUs by sharing vital information. Nevertheless, the challenge lies in navigating complex situations, especially in high-mobility areas. It is, therefore, important to have vehicle control systems that are efficient in making decisions while adapting to changing road conditions [6, 7]. Artificial intelligence (AI) shows promise for safer roads, with techniques like Deep Learning (DL) [8] and Deep Reinforcement Learning (DRL) at the forefront. DL excels in environment perception and object identification [9], while DRL methods are successful in tasks such as motion planning[10], decision-making [11], and vehicle control [12], enabling agents to learn and optimize actions based on interaction with their environment [5; 13]. Despite the significant advancement of AV technology, there still is a significant research gap in autonomous driving in relation to AV management in extremely complicated scenarios with high traffic density [14; 15]. More research is still needed to determine if AI systems are effective in such demanding scenarios. Traditional traffic management systems have difficulty dealing with the complexity and unpredictability of modern highways [16] in the face of growing traffic volumes in cities [17]. Under such circumstances, machine learning, particularly deep reinforcement learning, seems like a viable direction for developing intelligent vehicle control systems that can adapt to changing environments in real-time [18]. By combining deep neural networks with reinforcement learning, DRL allows autonomous agents to take optimal actions through trial and error. In advance of vehicle control, DRL has the potential to guide how vehicles navigate in traffic, make critical decisions, and respond to several challenges [19]. Benefiting from the power of DRL models, the advanced control systems autonomously adapt to changing traffic conditions by improving traffic efficiency and safety. The limitations of existing research reside in the absence of comprehensive efforts dedicated to guiding AVs in navigating roundabouts in dense traffic involving other vehicles, cyclists, and motorcycles. Additionally, the existing studies address simplistic scenarios, neglecting the complexities of real traffic in the city. To overcome these limitations, we propose a novel approach that aims to facilitate the entry of AVs into roundabouts, ensuring safety by minimizing the collision likelihood. In summary, the main contributions of this paper are as follows:  
1. We address the problem of entering roundabouts with dense traffic involving other vehicles, cyclists, and motorcycles. 2. We propose a modified SAC algorithm in combination with CNN to extract the main features of images captured by the front camera of the AV. We combine those features with the description vector to help the AV reach its destination quickly. 3. We compare our model with DQN and PPO algorithms.