The field of autonomous vehicle navigation and control is rapidly advancing, with a focus on developing more robust, efficient, and adaptive systems. Recent research has emphasized the importance of incorporating real-time disturbance estimation, dynamic graph generation, and neuro-symbolic approaches to improve motion planning and control. These innovations have led to significant improvements in trajectory tracking accuracy, task efficiency, and energy performance, as well as enhanced robustness to environmental disturbances and uncertainties. Notable papers in this area include: V*: An Efficient Motion Planning Algorithm for Autonomous Vehicles, which introduces a graph-based motion planner that integrates both motion dimensions directly into graph construction. Optimization of Flip-Landing Trajectories for Starship based on a Deep Learned Simulator, which proposes a differentiable optimization framework for flip-and-landing trajectory design of reusable spacecraft. Robust and Agile Quadrotor Flight via Adaptive Unwinding-Free Quaternion Sliding Mode Control, which presents a new adaptive sliding mode control framework for quadrotors that achieves robust and agile flight under tight computational constraints.