The field of autonomous vehicle research is moving towards a more integrated approach, combining aspects of privacy, fairness, and sustainability. Researchers are exploring novel algorithms and techniques to balance competing demands, such as traffic efficiency, environmental sustainability, and individual privacy. Notably, there is a growing interest in multi-objective reinforcement learning and topology-enhanced methods to optimize cooperative decision-making in complex traffic scenarios. In the area of location-based vehicular traffic management, new methods are being developed to protect sensitive geographical data while maintaining utility for traffic management and ensuring fairness across regions. Some noteworthy papers in this area include: The paper on Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System, which proposes a novel algorithm to address the challenges of balancing privacy, utility, and fairness. The paper on CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories, which introduces a novel privacy attack mechanism that leverages Controller Area Network messages to uncover driving trajectories. The paper on Topology Enhanced MARL for Multi-Vehicle Cooperative Decision-Making of CAVs, which proposes a topology-enhanced MARL method for optimizing cooperative decision-making of connected and autonomous vehicles.