# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 2 Related Work  
The reason behind the widespread interest in DRL algorithms is their exceptional data handling capacity and ability to capture intricate states in high-dimensional spaces, specifically in the robotics field [20]. Their application in Unmanned Ground Vehicles (UGV) [21] is particularly noteworthy; DRL algorithms are increasingly being investigated for autonomous navigation tasks. An example of such behavior in this domain is the number of works showing new ways of extracting decision-making and control commands from laser scans [22; 23]. What distinguishes them from other methods used is that they can smoothly switch between simulated models and actual situations that demonstrate good performance over time in terms of transferability. This further underscores the robustness and effectiveness of these algorithms in practical applications. According to [24], the study addresses the issue of mixed traffic involving human-driven vehicles (HVs) and AVs. It advocates for an approach based on deep Q-networking for managing AV movement through intersections aimed at enhancing the efficiency of road utilization as well as safety levels. It shows its superiority over existing methods, especially in dynamic traffic environments, effectively reducing driving distance, time, and waiting time for AVs. [PERSON] et al. [25] presents an approach that uses DRL to control AV driving through intersections with high traffic. Their DRL framework is implemented based on DQN to achieve an effective navigation strategy in the presence of other road users. The results show that their model reduces travel time delays while avoiding collisions when compared with existing traffic control methods. [PERSON] et al. [26] introduced a reinforcement-learning (RL) method for self-driving cars in complex traffic conditions. The paper aimed to speed up learning by using motion skills and the prior knowledge of experts. It used expert demonstrations and a double initialization strategy in order to optimize leveraging expert priors. According to their experiments, it is shown that this model outperforms other models with regard to learning efficiency and driving performance in heavy traffic. [PERSON] et al. [27] address the challenge of on-ramp merging for AVs in mixed traffic. They formulate the problem as a MARL task and develop an efficient MARL algorithm with features such as action masking, local rewards, curriculum learning, and parameter sharing. The proposed approach consistently outperforms state-of-the-art benchmarks in terms of training efficiency and collision rate. [PERSON] et al. [28] present a DRL-based lane-changing model for autonomous vehicles in mixed traffic. The model uses an analysis of natural driving trajectories to build a lane-changing environment at the vehicle group level. In order to mimic mixed flow situations, it uses the SAC algorithm to adjust velocity while taking into account the trajectories of surrounding vehicles. Training and testing on real-world data show that the model achieves a 90% success rate in lane-changing without a collision. In terms of safety and efficiency evaluation indicators, the system improves both safety and efficiency when compared to human driving. [PERSON] et al. [29] tackle the challenge of ensuring both safety and efficiency for AVs navigating unsignaled roundabouts. They propose a driving policy based on the SAC algorithm, enhanced with interval prediction and self-attention mechanisms, to focus on potential conflict vehicles and predict driving risks. The algorithm was trained in a low-dimensional environment and validated in the CARLA simulator. Results show a 15% reduction in collisions, improved safety, and effective decision-making at roundabout entrances and exits. However, their work is tested only in CARLA without training to catch more skills and did not include the perception model or take into account high mobility. The paper by [30] explores optimizing traffic dynamics with a mix of AVs and human-driven vehicles (HVs) using the PPO algorithm.