Researchers at the Department of Ocean Engineering, Indian Institute of Technology (IIT) Madras, have developed SEANAV, a real-time digital twin platform engineered to test, validate, and train autonomous maritime navigation systems, enabling vessels to navigate more safely across complex ocean environments. The development, published in the Journal of Offshore Mechanics and Arctic Engineering (ASME), represents a significant step forward for AI-driven shipping safety.

What SEANAV Does

SEANAV integrates Artificial Intelligence (AI), Virtual Reality (VR), and multi-sensor fusion to construct a detailed virtual ocean environment built on a Unity-based simulation engine. The platform recreates realistic ocean waves, dynamic shipping traffic, and adverse weather conditions, allowing autonomous vessels to execute AI-driven collision avoidance and path-following manoeuvres before they ever set sail. Multiple ships can navigate congested virtual waterways simultaneously, and the system also provides an immersive VR interface for training maritime professionals.

According to the research team, conducting thousands of hours of virtual simulation substantially reduces testing costs while promoting safer and more sustainable shipping operations — a compelling case for the platform's commercial and regulatory relevance.

Bridging Gaps in Maritime Vision Datasets

A key challenge the team addressed was the inadequacy of existing maritime vision datasets. Widely used resources such as SeaShips and HRSC2016, researchers noted, lack sufficient variation in viewpoints and weather conditions, which limits the real-world reliability of deep learning models designed for maritime navigation. To overcome this gap, the team developed a novel custom dataset called 'DetectShips', comprising 14,000 real ship images, and combined it with the SeaShips dataset to train a computer vision algorithm integrated with stereo sensors.

StereoLiD: A Hybrid Sensing Framework

Beyond the dataset, the researchers introduced 'StereoLiD', a hybrid sensing framework that fuses stereo vision cameras with LiDAR data sourced from the Unity simulation platform. In this architecture, stereo cameras detect and classify vessels to estimate relative depth, while LiDAR inputs further refine depth estimation accuracy. Data from both sensors are then fused using a Kalman filter, achieving precise target localisation even in challenging conditions.

According to the researchers, this multi-vision sensor fusion approach overcomes common limitations of conventional optical sensors — including fluctuating lighting and dense fog — ensuring dependable target detection and navigation across complex oceanic environments.

Academic Recognition and Global Reach

The research has not only been published in a peer-reviewed ASME journal but was also presented at ASME OMAE conferences in Vancouver and Tokyo, signalling international validation of the platform's scientific merit. The dual recognition underscores IIT Madras's growing profile in ocean engineering research with real-world applications for global shipping.

What Comes Next

As autonomous vessels inch closer to commercial deployment worldwide, platforms like SEANAV could become essential infrastructure for regulatory certification and crew training. The integration of increasingly sophisticated AI with simulation-based safety frameworks is widely seen as a prerequisite for the maritime industry's autonomous future, and IIT Madras's contribution positions India as a credible player in that transition.