The field of autonomous vehicle navigation and control is rapidly advancing, with a focus on improving safety, efficiency, and adaptability in various environments. Recent developments have centered around the integration of machine learning, reinforcement learning, and model predictive control to enhance trajectory prediction, collision risk assessment, and motion planning. Notably, researchers are exploring the use of transformer-based frameworks, hybrid reinforcement learning, and robust model predictive control to address the complexities of multi-vessel interactions, dynamic waterways, and confined spaces. These innovative approaches have demonstrated significant improvements in forecasting capabilities, fuel efficiency, and navigation precision. Some noteworthy papers include: Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment, which presents a novel framework for predicting vessel trajectories and assessing collision risks. Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation, which proposes a reinforcement learning solution for routing vessels through dynamic waterways. Hybrid Reinforcement Learning and Search for Flight Trajectory Planning, which explores the combination of reinforcement learning and search-based path planners for optimizing flight paths. Safe Robust Predictive Control-based Motion Planning of Automated Surface Vessels in Inland Waterways, which presents a new motion planning approach for autonomous surface vessels using robust model predictive control and control barrier functions. Dynamic Modeling and Efficient Data-Driven Optimal Control for Micro Autonomous Surface Vehicles, which introduces a physics-driven dynamics model and a data-driven optimal control framework for micro autonomous surface vehicles.