The field of autonomous driving is rapidly advancing, with a focus on improving road safety and efficiency. Recent research has highlighted the importance of considering the behavior of surrounding vehicles, particularly in high-risk edge cases such as emergency braking in dense traffic. Novel longitudinal control and collision avoidance algorithms, leveraging techniques like deep reinforcement learning, have shown significant promise in preventing potential pile-up collisions. Additionally, the integration of domain randomization and model-based control has improved the robustness and reliability of control systems in complex environments. Socially-aware autonomous driving is also gaining traction, with algorithms being developed to infer yielding intentions and predict future motions of surrounding vehicles. Noteworthy papers include: Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving, which proposes a novel algorithm that integrates adaptive cruising with emergency braking. Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems, which presents a novel quantum-classical hybrid framework that synergizes quantum with classical reinforcement learning for dynamic path planning.