Autonomous and Semi-Autonomous Marine Vessels: Engineering Systems & AI Control

Introduction

The transition from human-led command to AI-assisted navigation is perhaps the most disruptive shift in maritime history. The focus is on Level 4 autonomy for short-sea shipping and “Blue Highways,” where labor shortages and the high cost of crew drive the demand for unmanned operations.

In emerging economies, autonomous technology is often applied in a semi-autonomous capacity to enhance safety and bridge the skills gap in rapidly expanding maritime sectors. Here, AI acts as a “safety co-pilot,” assisting less experienced crews in navigating complex, congested waterways. This course examines the control architectures and AI navigation models that make these levels of autonomy possible.

The challenge in autonomous shipping is not just technical but regulatory and ethical. As we move toward a world where AI makes split-second collision avoidance decisions, leaders must understand algorithmic accountability.

Learning Outcomes

1. Understand AI control frameworks for safe navigation in congested waters.
2. Assess regulatory and liability challenges of autonomous systems across jurisdictions.
3. Implement AI-based collision avoidance systems that are COLREGs-compliant.
4. Develop remote-operator “Psychological Safety” protocols for navigation.
5. Design redundant engineering systems for unmanned or reduced-crew engine rooms.
6. Evaluate the cost-benefit of autonomous vs. crewed operations for regional trade.

Learning Objectives

1. Analyse autonomous vessel architectures, focusing on fail-safe AI loops.
2. Evaluate AI navigation models against real-world maritime safety data.
3. Assess safety and regulatory challenges in alignment with IMO and WEF standards.
4. Differentiate between the four levels of maritime autonomy.
5. Simulate an AI response to a sensor failure in a semi-autonomous ship.
6. Explain the ethical implications of autonomous decision-making in emergencies.