Texas A&M Develops SMART-SEA AI Collision Avoidance System

Texas A&M University researchers published development of SMART-SEA, an AI-powered collision avoidance system providing real-time maneuvering recommendations through machine learning analysis of raw radar imagery combined with vessel maneuverability models calibrated to seafarer experience levels. The system, tested aboard research vessel Trident, addresses the IMO-recognized reality that human error causes nearly 70% of maritime accidents. Machine learning radar analysis: Unlike conventional collision avoidance relying on AIS transponder data (which can be disabled, spoofed, or absent on smaller vessels), SMART-SEA processes raw radar returns to detect stationary hazards including oil platforms, wellheads, coastlines, and bridges that don't transmit identification signals. The ML algorithms identify and track objects in all weather conditions, maintaining detection capability when visual observation is degraded and conventional radar interpretation becomes more challenging. Experience-calibrated recommendations: The system incorporates computational fluid dynamics vessel models calibrated against seafarer experience levels, providing recommendations appropriate to the operator's skill and the vessel's actual maneuvering characteristics. A less experienced officer receives different guidance than a seasoned master, while recommendations account for how specific vessels actually respond rather than assuming generic ship handling capabilities. This human-factors integration maintains the "human-in-command" design philosophy—visual and audible dashboard guidance supports decision-making while the seafarer controls steering execution. Development collaboration: The project involved former seafarers, Texas A&M Galveston faculty, U.S. Navy, and U.S. Coast Guard input, ensuring practical operational relevance rather than purely academic optimization. Published in Process Safety and Environmental Protection journal, the research establishes scientific validation for the approach. The system's low-cost design potentially enables adaptation for recreational vessels—a market segment where navigation assistance technology could prevent accidents among less experienced operators. For commercial vessel operators, SMART-SEA represents academic research progressing toward practical bridge decision support. [Texas A&M Stories]

This story is part of the Maritime AI Digest of 4 January 2026.