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Maritime collisions remain a stubborn and costly problem. Despite modern sensors and navigation aids, ships still strike offshore platforms, port infrastructure, and anchored vessels with worrying frequency. In many cases, investigations point to human error rather than technical failure. Large ships are slow to respond, require long stopping distances, and operate in environments shaped by wind, currents, and limited visibility. When decisions come too late, even minor misjudgments can lead to serious damage.
A new AI-based navigation system aims to address that gap by supporting, rather than replacing, human decision-making at sea. Developed by researchers at Texas A&M University, the system—known as SMART-SEA—acts as an intelligent co-captain that provides real-time collision avoidance guidance to ship operators. Instead of fully autonomous control, it follows a “human-in-the-loop” approach, offering recommendations that crews can assess and act on immediately.
The system is designed to reduce reliance on individual experience alone, especially in complex or unfamiliar situations. The system continuously analyzes the vessel’s motion, surrounding traffic, and nearby structures, then generates clear instructions to reduce collision risk. This approach is particularly relevant when navigating near stationary objects such as oil platforms, wind turbines, and port facilities, where maneuvering space is limited and errors are unforgiving.
According to Interesting Engineering, at the technical level, the system combines raw radar imaging with advanced machine learning. Radar data allows the system to detect objects in poor visibility and adverse weather, while AI algorithms classify stationary hazards that may not appear as immediate threats on conventional displays. The system also incorporates computational fluid dynamics models and historical vessel motion data, enabling it to account for inertia, turning radius, and environmental forces.
To reflect real-world practices, the decision logic was shaped by input from experienced seafarers. Their operational knowledge was used to train models that assess risk and suggest maneuvers that remain compliant with international collision-avoidance regulations. By aligning AI outputs with established maritime rules, the system is intended to be intuitive rather than disruptive for crews.
From a defense and homeland security perspective, the technology has clear implications. Naval vessels, coast guard ships, and logistics fleets often operate near critical infrastructure and in congested waterways. An AI assistant that improves situational awareness and reduces collision risk could enhance safety during routine operations, patrols, and port approaches. It may also prove valuable for auxiliary and support vessels that lack advanced combat navigation systems.
The system reflects a broader trend toward decision-support AI in safety-critical domains. By combining radar, physics-based modeling, and human expertise, it demonstrates how artificial intelligence can reduce risk at sea without removing humans from the loop.
