The field of autonomous vehicles is moving towards more efficient and safe communication and planning strategies. Researchers are exploring the use of uncertainty-aware approaches, such as deep reinforcement learning, to improve cooperative motion planning and reduce the impact of perception, planning, and communication uncertainties. Another area of focus is the optimization of communication control factors to balance safety and energy consumption in rural areas. The use of vehicle-to-vehicle communication and cooperative awareness messages is also being investigated to enhance situational awareness and predict vehicle trajectories. Noteworthy papers include: UNCAP, which proposes a vision-language model-based planning approach that enables cooperative autonomous vehicles to communicate via lightweight natural language messages while accounting for perception uncertainty. CAMNet, which designs and trains a neural network to leverage cooperative awareness messages for vehicle trajectory prediction.