This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planning and control methods in autonomous racing with vehicle dynamics models briefly mentioned. However, previous surveys overlook the importance of vehicle dynamics under challenging conditions of top speeds and non-steady state driving, which are unique characteristics in autonomous racing. Recognizing the vital role of vehicle dynamics modeling in an autonomous racecar's prediction, planning, and control modules, this survey seeks to ascertain to what degree the nominal full-scale racecar dynamics can be streamlined without sacrificing accuracy for simplicity. Furthermore, this survey provides essential guidance for organizers of virtual autonomous races, helping them choose vehicle dynamics models that meet the required level of precision. This paper begins with a review of previous surveys on vehicle dynamics modeling, highlighting their limitations in the context of autonomous racing. Following this, it investigates the existing dynamics models for autonomous racing vehicles, along with a comprehensive examination of the existing physical/virtual testing platforms. The paper concludes by discussing emerging trends and offering perspectives in the field of vehicle dynamics modeling for autonomous racing, paving the way for groundbreaking research and innovations in autonomous racing.

Parking in tiny and cluttered slots is a critical challenge for automated parking systems, where the planning module serves as a core component responsible for generating collision-free and kinematically feasible trajectories. However, existing methods face significant limitations, such as geometric-based approaches generating paths rather than trajectories, mass point models failing in tiny berths, and full-dimensional models with large-scale constraints lead to time-intensive computations. To address these issues, this paper proposes an anytime trajectory optimizer for accurately parking an autonomous vehicle in tiny spaces cluttered with obstacles. The proposed method incorporates differentiable collision-avoidance constraints and a trust region strategy to improve computational efficiency. It can also iteratively refine the solution, enabling the optimizer to converge to a new minimum by breaking previous constraints and establishing new ones. Extensive simulations show the proposed method outperforms mainstream planners, effectively handling cluttered environments and tiny parking slots where others fail, or outperforming them in speed when they succeed. It reduces runtime by 90.19% compared to the baseline, while ensuring feasibility and continuous optimization.

Motion planning for on-road autonomous driving requires generating locally accurate spatio-temporal trajectories over a finite horizon, while facing increasing uncertainty and interaction variability toward distant regions. However, most existing planners employ uniform planning accuracy along the horizon, which implicitly treats far-field predictions with the same fidelity as near-term execution. This uniform treatment often leads to unnecessary computational effort and reduced planning efficiency without improving near-field feasibility. This paper presents an economical motion planning framework that allocates planning accuracy according to the spatio-temporal distance from the ego vehicle. The framework preserves high-fidelity planning in the near field where execution is imminent, while progressively reducing resolution and solution depth in the far field where uncertainty dominates and replanning is expected. A two-stage architecture is adopted, combining a distance-aware search for coarse path and velocity generation with distance-sensitive numerical refinement that prioritizes near-field feasibility under receding horizon execution. The simulation results demonstrate improved computational efficiency and planning reliability compared with uniform resolution baselines. Real-world experiments validate the stable online replanning performance in dynamic environments.