Waypoints Matter: A Systematic Study for Sampling-Based Trajectory Planning

2026-06-04

AI Summary

This study addresses the lack of systematic investigation into how waypoint placement affects planning performance in existing sampling-based trajectory planners. Treating waypoint placement as a core design variable, the work systematically evaluates uniform sampling, an improved Ramer–Douglas–Peucker algorithm (RDP*), and a proposed curvature-aware allocation strategy across 449 scenarios spanning five CommonRoad map types, while keeping trajectory primitives and candidate counts fixed. The nominal spacing \(d_s\) is introduced as the primary tuning parameter. Results demonstrate that \(d_s\) is the dominant factor influencing performance; with appropriate tuning, uniform sampling consistently matches or outperforms alternative methods. The curvature-aware strategy offers marginal benefits only in high-curvature segments, whereas RDP* fails to surpass uniformly spaced waypoints in any tested configuration.

Abstract

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.

Problem

• waypoint placement
• sampling-based trajectory planning
• autonomous driving
• trajectory feasibility
• path planning

Innovation

• waypoint placement
• sampling-based planning
• trajectory planning
• curvature-conditioned allocation
• autonomous driving