"Why This Avoidance Maneuver?" Contrastive Explanations in Human-Supervised Maritime Autonomous Navigation
2026-04-09 • Artificial Intelligence
Artificial IntelligenceRobotics
AI summaryⓘ
The authors studied how to make automated ship collision avoidance systems easier to understand for human supervisors, especially those with maritime experience. They created a way to explain the system's decisions by comparing the chosen maneuvers with other possible options, using both pictures and text. Their small study with marine officers showed that these comparisons help explain the system's goals but can also make the decision process feel more complicated. The authors suggest future systems should provide explanations only when needed or tailored to specific situations to avoid overwhelming users.
Automated collision avoidanceMaritime navigationHuman supervisionContrastive explanationsCognitive workloadUser studyVisual cuesTextual explanationsMulti-vessel encounters
Authors
Joel Jose, Andreas Madsen, Andreas Brandsæter, Tor A. Johansen, Erlend M. Coates
Abstract
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.