UDAV：不确定性驱动的自适应VLM航路点规划器

中文摘要 AI 辅助

视觉语言模型（VLMs）可以直接从航空影像生成越野导航的路线，但其预测未提供可靠性指示。我们提出UDAV，一种用于无人机引导无人地面车辆导航的不确定性驱动的自适应VLM航路点规划器。UDAV生成多个随机轨迹预测，选择其中位数作为自洽的名义路线，并根据其空间离散度估计预测不确定性。当内部航路点上的最大不确定性超过阈值时，UDAV启动重新考虑阶段；否则，直接返回中位数路线。我们在来自两次无人机飞行的400个留出轨迹查询上评估UDAV。随机中位数选择将平均位移误差（ADE）从确定性预测的147.4像素降至115.9像素。完整规划器实现了110.4像素的平均ADE，相比确定性规划降低了25.1%，同时为所有查询生成了有效轨迹。UDAV在所有评估配置中（包括更高预算的K=10共识基线）也实现了最低的第90和第95百分位误差。相对于K=5中位数，UDAV将这些误差分别从225.3和326.0像素降至199.0和290.8像素。这些结果表明，随机VLM预测既提供了更强的名义路线，也提供了可操作的不确定性信号，用于选择性地缓解大型规划误差。

英文摘要

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.

发表机构

• National Research Council Canada（加拿大国家研究委员会）

机构由 AI 辅助整理，请以论文原文为准。