UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner

2026-09-14

AI Summary

为解决VLMs在越野导航中预测可靠性问题，UDAV通过多次随机轨迹预测、选择中位数路径及评估不确定性来规划路径，有效降低了平均位移误差。

Abstract

Vision-language models (VLMs) can generate routes directly from aerial imagery for off-road navigation, but their predictions provide no indication of reliability. We present UDAV, an Uncertainty-Driven Adaptive VLM Waypoint Planner for UAV-guided UGV navigation. UDAV draws multiple stochastic trajectory predictions, selects their medoid as a self-consistent nominal route, and estimates predictive uncertainty from their spatial dispersion. When the maximum uncertainty across interior waypoints exceeds a threshold, UDAV invokes a reconsideration stage; otherwise, it returns the medoid directly. We evaluate UDAV on 400 held-out trajectory queries from two UAV flights. Stochastic medoid selection reduces the mean average displacement error (ADE) from 147.4 pixels for a deterministic prediction to 115.9 pixels. The complete planner achieves a mean ADE of 110.4 pixels, a 25.1% reduction relative to deterministic planning, while producing valid trajectories for all queries. UDAV also yields the lowest 90th- and 95th-percentile errors among all evaluated configurations, including a higher-budget K=10 consensus baseline. Relative to the K=5 medoid, UDAV reduces these errors from 225.3 and 326.0 pixels to 199.0 and 290.8 pixels, respectively. These results demonstrate that stochastic VLM predictions provide both a stronger nominal route and an actionable uncertainty signal for selectively mitigating large planning errors.

Problem

• Uncertainty
• Vision-language Models
• Off-road Navigation
• Waypoint Planner

Innovation

• Uncertainty-Driven
• Adaptive VLM
• Waypoint Planner
• Stochastic Trajectory Predictions
• Medoid Selection