The field of autonomous vehicle control is moving towards more sophisticated and efficient control strategies. Researchers are exploring novel approaches that combine traditional control methods with machine learning and optimization techniques to improve the performance and robustness of autonomous vehicles. One notable trend is the integration of model predictive control (MPC) with other control methods, such as deep reinforcement learning and linear parameter varying (LPV) control, to achieve better tracking accuracy and adaptability. Another area of focus is the development of reactive controllers that can handle complex scenarios and avoid common issues such as dead-ends and incomplete boundaries. Noteworthy papers include DTR, which presents a reactive controller that combines Delaunay triangulation with track boundary segmentation to achieve 70% faster lap times, and LoL-NMPC, which proposes a novel NMPC formulation that incorporates low-level controller dynamics to minimize trajectory tracking errors.