为何这种避让动作？
"Why This Avoidance Maneuver?" Contrastive Explanations in Human-Supervised Maritime Autonomous Navigation
AI总结：
本文探讨如何通过对比解释帮助海上导航员理解避让决策，提出生成对比解释的方法，并通过用户研究验证其在复杂多船场景中的有效性及认知负荷影响。
摘要：
自动化海上碰撞避让将依赖人类监督。这需要透明地展示系统如何感知场景并规划避让动作。然而，避让动作背后的因果逻辑往往复杂难懂。本文探讨如何以选择性、易懂的方式向有航海背景的监督者解释这些因素。我们提出一种生成对比解释的方法，通过将系统提出的解决方案与相关替代方案进行比较，提供以人类为中心的见解。为评估此方法，我们开发了一个框架，利用视觉和文本提示突出状态最先进的碰撞避让系统的关键目标。一项包含四位经验丰富的海员的探索性用户研究表明，对比解释有助于理解系统的目标。然而，我们的发现也表明，尽管这些解释在复杂多船相遇中非常有价值，但它们可能增加认知负荷，表明未来海上界面可能最受益于需求驱动或场景特定的解释策略。
英文摘要：
Automated maritime collision avoidance will rely on human supervision for the foreseeable future. This necessitates transparency into how the system perceives a scenario and plans a maneuver. However, the causal logic behind avoidance maneuvers is often complex and difficult to convey to a navigator. This paper explores how to explain these factors in a selective, understandable manner for supervisors with a nautical background. We propose a method for generating contrastive explanations, which provide human-centric insights by comparing a system's proposed solution against relevant alternatives. To evaluate this, we developed a framework that uses visual and textual cues to highlight key objectives from a state-of-the-art collision avoidance system. An exploratory user study with four experienced marine officers suggests that contrastive explanations support the understanding of the system's objectives. However, our findings also reveal that while these explanations are highly valuable in complex multi-vessel encounters, they can increase cognitive workload, suggesting that future maritime interfaces may benefit most from demand-driven or scenario-specific explanation strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
