Real-time autonomous driving commonly relies on sampling-based trajectory planners that link candidate trajectories to target waypoints along the road centerline. The placement of these waypoints directly impacts both the existence and quality of feasible trajectories. Yet, its effect on planner performance remains largely unexplored. In this paper, we treat waypoint placement as a first-class design variable. We hold the trajectory primitive and candidate budget fixed, and systematically sweep three placement strategies (uniform spacing, an augmented Ramer-Douglas-Peucker variant (RDP*), and a novel curvature-conditioned allocation) across 449 configurations and five CommonRoad maps of increasing geometric complexity. Our results show that the nominal inter-waypoint spacing $d_s$ is the primary performance driver, with large differences in planner reliability attributed to placement alone. Uniform sampling at a well-tuned spacing matches or surpasses both RDP* and the centered curvature variant. The curvature variant offers a small but consistent advantage on geometrically complex roads under reliability-first and balanced weightings, while RDP* never outperforms uniform sampling. These findings suggest that $d_s$ should be treated as the dominant tuning parameter, with geometry-aware strategies reserved for curvature-rich corridors where feasibility is the limiting factor.

翻译：实时自动驾驶通常依赖基于采样的轨迹规划器，这类规划器将候选轨迹与沿道路中心线的目标路径点联系起来。这些路径点的布设直接决定了可行轨迹的存在性及其质量。然而，其对规划器性能的影响尚未得到充分探索。本文将路径点布设作为首要设计变量，在固定轨迹基元与候选预算的前提下，系统性地对三种布设策略（均匀间距、一种增强型Ramer-Douglas-Peucker变体（RDP*）以及一种新颖的曲率条件分配）在449种配置及五张几何复杂度递增的CommonRoad地图上进行了遍历。结果表明，名义路径点间距 $d_s$ 是影响性能的首要因素，仅凭布设方式的差异即可导致规划器可靠性的显著变化。在调优间距下的均匀采样能够达到或超越RDP*及中心曲率变体的性能。曲率变体在可靠性优先及平衡加权条件下，对几何复杂道路具有微小但一致的优势，而RDP*从未超越均匀采样。这些发现表明，$d_s$ 应被视为主导调优参数，而几何感知策略应保留给通行能力构成限制的曲率密集通道。