The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan without approval. Meeting these obligations in the alert-to-takeover gap calls for a short-horizon, human-overridable fallback maneuver. Classical maritime autonomy stacks struggle when the correct action depends on meaning (e.g., diver-down flag means people in the water, fire close by means hazard). We argue (i) that vision-language models (VLMs) provide semantic awareness for such out-of-distribution situations, and (ii) that a fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver makes this practical in the handover window. We introduce Semantic Lookout, a camera-only, candidate-constrained VLM fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority. On 40 harbor scenes we measure per-call scene understanding and latency, alignment with human consensus (model majority-of-three voting), short-horizon risk-relief on fire hazard scenes, and an on-water alert->fallback maneuver->operator handover. Sub-10 s models retain most of the awareness of slower state-of-the-art models. The fallback maneuver selector outperforms geometry-only baselines and increases standoff distance on fire scenes. A field run verifies end-to-end operation. These results support VLMs as semantic fallback maneuver selectors compatible with the draft IMO MASS Code, within practical latency budgets, and motivate future work on domain-adapted, hybrid autonomy that pairs foundation-model semantics with multi-sensor bird's-eye-view perception and short-horizon replanning. Website: kimachristensen.github.io/bridge_policy

翻译：国际海事组织（IMO）《海上自主水面船舶（MASS）规则》草案要求自主和远程监控的海事船舶能够检测其偏离操作设计域的情况，进入预定义的故障安全状态以通知操作员，允许立即的人工接管，并在未经批准时避免更改航行计划。要在“警报至接管”的间隙内履行这些义务，需要一种短时域、可由人工接管的故障安全机动策略。当正确的行动取决于语义时（例如，潜水旗意味着水中有人，附近起火意味着危险），传统的海事自主技术栈难以应对。我们认为：（i）视觉语言模型（VLMs）为此类分布外情况提供了语义感知能力；（ii）采用一种包含短时域、可人工接管故障安全机动的快慢异常检测流程，可在交接窗口内实现这一目标。我们提出了Semantic Lookout，一种仅使用摄像头、候选约束的VLM故障安全机动选择器，它在持续人工授权下，从水域有效、世界坐标系锚定的轨迹中选择一个谨慎动作（或保持原位）。我们在40个港口场景中评估了每次调用的场景理解能力与延迟、与人类共识（三人多数投票模型）的一致性、火灾危险场景的短时域风险缓解效果，以及一次水上“警报->故障安全机动->操作员接管”的全过程。延迟低于10秒的模型保留了较慢的先进模型的大部分感知能力。该故障安全机动选择器优于仅基于几何信息的基线方法，并在火灾场景中增加了安全距离。一次实地运行验证了端到端操作的可行性。这些结果表明，视觉语言模型可作为语义故障安全机动选择器，与国际海事组织MASS规则草案兼容，且在实用的延迟预算内，并激励未来研究领域自适应、混合自主系统，将基础模型的语义能力与多传感器鸟瞰图感知及短时域重规划相结合。项目网站：kimachristensen.github.io/bridge_policy