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.
Sun et al. (2026) studied this question.