The field of autonomous vehicles is rapidly advancing, with a focus on improving control and navigation systems. Recent research has emphasized the importance of accurate mass estimation, terrain traversability, and head stabilization in wheeled bipedal robots. Innovative approaches, such as implicit neural representation and force-estimation-based admittance control, are being developed to address these challenges. Additionally, advancements in model predictive control (MPC) frameworks are enabling emergent locomotion and loco-manipulation capabilities in legged robots. These developments are paving the way for more efficient, stable, and adaptable autonomous vehicles. Noteworthy papers include: Optimizing the Driving Profile for Vehicle Mass Estimation, which presents a framework for designing driving profiles to support accurate mass estimation. Off-Road Navigation via Implicit Neural Representation of Terrain Traversability, which introduces a novel approach to estimating terrain traversability using implicit neural representation. Reference-Free Sampling-Based Model Predictive Control, which enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences. Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation, which proposes a whole-body MPC framework for coordinated whole-body motion in loco-manipulation tasks.