Autonomous racing has expanded markedly in recent years, bringing renewed focus to planning and control at the limits of performance. Racing platforms are complex, strongly nonlinear dynamical systems whose behavior varies with speed,  and tire–road interaction. While planning and race strategy are well established in classical (human-driven) motorsport, trajectory optimization tailored to fully autonomous race vehicles is comparatively nascent, with distinct requirements arising from the perfect knowledge of the autonomous driver behavior. Control algorithms, on the other hand, must operate near stability margins, while being robust to variations of the working conditions. Because feasible trajectories and the controllers that track them are tightly coupled through these nonlinearities and constraints, treating them sequentially can yield suboptimal or fragile solutions. Consequently, systematic exploration of co-design is essential to achieve repeatable, safe, and competitive autonomous racing performance.