Collaborative Work with Highly Automated Marine Navigation Systems

Area of Science:

• Maritime technology
• Human-computer interaction
• Artificial Intelligence (AI)

Background:

• AI integration in navigation aims to improve efficiency and decision-making.
• Understanding human-AI collaboration is crucial for effective AI system design in maritime operations.

Purpose of the Study:

• To investigate the transition towards increased machine autonomy in the maritime sector.
• To examine the discrepancies in understanding human-AI collaboration between technology designers and navigators.

Main Methods:

• Empirical study involving interviews with 9 technology designers and 5 navigators.
• Field observations conducted aboard two partially automated ferries.
• Analysis of navigators' roles and designers' perspectives on AI integration.

Main Results:

• A significant gap exists between designers' and navigators' views on human-AI cooperation.
• Navigators perceive their role as 'backup,' involving ad-hoc control takeovers.
• Designers' models often underestimate the navigators' in-situ skills and heuristic decision-making.

Conclusions:

• AI system design should better align with human cooperation by increasing visibility of computational activities.
• Incorporating social cues in AI design can better articulate human work in natural settings.
• Findings offer insights for human-AI interaction design in future navigation and control room work.