The recent advancements in autonomous vehicle technology have seen a significant shift towards more efficient, safe, and fair multi-agent systems. Researchers are increasingly focusing on integrating advanced machine learning techniques, such as Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS), to address the complexities of decision-making in dynamic environments. These methods are being tailored to optimize coordination among vehicles, ensuring not only system efficiency but also fairness among agents, which is crucial in resource-constrained scenarios like urban mobility. Additionally, the incorporation of real-time communication systems, such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X), is revolutionizing traffic planning and collision avoidance, enhancing both computational efficiency and safety. The field is also witnessing innovations in trajectory planning and control, with semi-decentralized and variational-equilibrium-based approaches proving effective in managing complex interactions among autonomous vehicles. Notably, the development of embeddable Ising machines and quantum-inspired algorithms is providing new tools for solving combinatorial optimization problems in real-time tracking systems. These developments collectively point towards a future where autonomous vehicles operate with greater autonomy, safety, and fairness, thanks to the integration of cutting-edge computational and communication technologies.