Decision making (DM) is essential and proven to be a natural and inherent part of the success of transport systems, particularly given the fast growth of autonomous systems in transport. It is critical but remains challenging to understand and predict DM performance in transport, because operators’ mental states have not been effectively considered in complex DM processes such as ship anti-collision operations. This paper proposes an advanced decision support methodology that pioneers the incorporation of objective neurophysiological and subjective data to analyse functional connectivity in the brain and predict DM performance in ship navigation. Experiments were conducted using a functional Near-Infrared Spectroscopy (fNIRS) technology to explore the functional connectivity of two groups (low workload and high workload) and predict their DM performance in a ship collision avoidance situation. It brings brain science into transport engineering and the results generate new contributions to the existing knowledge, including (1) the establishment of a methodology to detect different workload levels in safety–critical transport systems using psychophysiological measurement; (2) analysis of brain’s functional connectivity of different groups of decision makers (e.g., seafarers) with high and low workload tasks; (3) an advanced methodology to assess human reliability in complex scenarios and predict operational behaviours; (4) pioneering a human-centred approach to predict DM performance and demonstrate its feasibility in shipping. From a practical perspective, stakeholders can utilise the findings of this study to rationally evaluate human performance in transport system operations, aiding in operator qualification and certification processes. Furthermore, it is critical for adaptive automation regarding DM support in safety–critical systems.

中文翻译：

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为航运安全提供高级决策支持：功能连接分析


决策 （DM） 是必不可少的，并被证明是交通系统成功的自然和内在组成部分，特别是考虑到交通领域自动驾驶系统的快速增长。理解和预测运输中的 DM 性能至关重要，但仍然具有挑战性，因为在复杂的 DM 流程（如船舶防撞操作）中没有有效考虑操作员的精神状态。本文提出了一种先进的决策支持方法，该方法率先结合客观的神经生理学和主观数据来分析大脑中的功能连接并预测船舶导航中的 DM 性能。使用功能性近红外光谱 （fNIRS） 技术进行实验，以探索两组 （低工作量和高工作量） 的功能连接性，并预测它们在船舶避碰情况下的 DM 性能。它将脑科学引入运输工程，结果为现有知识做出了新的贡献，包括 （1） 建立一种方法，使用心理生理学测量来检测安全关键型运输系统中的不同工作负载水平;（2） 分析具有高和低工作量任务的不同决策者群体（例如海员）的大脑功能连接;（3） 一种先进的方法，用于评估复杂场景中的人类可靠性并预测操作行为;（4） 开创一种以人为本的方法来预测 DM 性能并展示其在航运中的可行性。从实践的角度来看，利益相关者可以利用这项研究的结果来理性地评估人类在运输系统运营中的表现，从而协助操作员资格认证和认证过程。 此外，在安全关键系统中，关于 DM 支持的自适应自动化也至关重要。