无人机-无人地面车辆协同系统的置信门控纯视觉航向对准
Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems
- North Carolina Agricultural and Technical State University（北卡罗来纳农工州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究无人机-无人地面车辆协同系统中基于视觉的航向预测命令发布决策问题，提出轻量级置信门控框架，利用边界框面积和预测航向短窗口变化作可靠性代理，实验表明其在多方面有权衡，能改善命令级行为。
AI中文摘要：
基于视觉的航向预测对无人机-无人地面车辆协作很有用，但仅准确预测并不能保证每个预测航向都应直接作为控制命令发布。本文研究了何时以及如何信任基于视觉的固定航向预测器进行命令发布的决策问题。提出了一个轻量级置信门控框架，其中使用从感知流导出的两个可解释的可靠性代理进行执行决策：作为与可见性相关代理的边界框面积和作为与稳定性相关代理的预测航向的短窗口变化。在低置信区间内，该框架将基线冻结-HOLD策略与有界混合回退进行比较，后者会保守地更新发布的命令。该方法在真实无人机-无人地面车辆数据集上在干净和受干扰条件下进行了评估。结果表明，置信门控在执行率、执行帧精度、发布命令精度和平滑度之间产生了明显的权衡。结果还表明，在基线冻结-HOLD策略下，稀疏执行会导致严重的过时命令错误，而有界混合回退在相同的门控决策下显著改善了命令级行为。这些发现突出表明，可靠的感知驱动自主性不仅取决于预测精度，还取决于低置信度期间的决策感知命令发布。
英文摘要：
Vision-based heading prediction is useful for UAV--UGV cooperation, but accurate prediction alone does not guarantee that every predicted heading should be issued directly as a control command. This paper investigates the decision problem of when and how a fixed vision-based heading predictor should be trusted for command issuance. A lightweight confidence-gated framework is proposed in which execution decisions are made using two interpretable reliability proxies derived from the perception stream: bounding-box area as a visibility-related proxy and short-window variation in predicted heading as a stability-related proxy. During low-confidence intervals, the framework compares the baseline freeze-HOLD policy with a bounded-blend fallback that updates the issued command conservatively. The method is evaluated on a real UAV--UGV dataset under clean and perturbed conditions. The results show that confidence gating creates a clear trade-off among execution rate, executed-frame accuracy, issued-command accuracy, and smoothness. The results further show that sparse execution can cause severe stale-command error under the baseline freeze-HOLD policy, whereas the bounded-blend fallback substantially improves command-level behavior under the same gate decisions. These findings highlight that reliable perception-driven autonomy depends not only on prediction accuracy, but also on decision-aware command issuance during low-confidence
