Intelligence in vehicles and other machinery is a result of a myriad of critical subsolutions. These often depend on AI models interpreting the messy real-world with the ability to predict what will happen next. This is done through gaining situational awareness through sensor fusion, be it based on mathematical modeling or deep learning.
The automotive environment requires split-second decisions to avoid and mitigate accidents. A self-driving car must sense, plan and act at least in half a second.
Marine vehicles move slowly but steadily and require precise decisions performed by the system. A miscalculation could mean that a large container ship is fated to wreck in a few minutes, regardless of efforts.
Some major-scale mining has operated largely autonomously for a decade, and port cranes, forest machines, tugs, and other machinery are hastily preparing for the same.
Read our eBook to learn about deep learning and mathematical modelling as the basis for sensor fusion, and how building situational awareness varies from one environment to another.
Situational awareness holds hundreds of smaller challenges within it. However, with AI technologies, we’re accelerating the speed at which we solve them for a wide range of industries from forestry, mining, and marine to logistics and manufacturing.
We’ve built intelligent mobile machines for widely varying environments and use cases.
Watch the webinar recording
CEO, Groke Technologies
Developer Marketing, NVIDIA
Deep Learning Start-up Account Manager, NER at NVIDIA
PhD, Senior AI Scientist, Silo AI
Watch the webinar recording
CEO, Groke Technologies
Developer Marketing, NVIDIA
Deep Learning Start-up Account Manager, NER at NVIDIA
PhD, Senior AI Scientist, Silo AI
In this article, I will cover the most common use cases I’ve seen for sensor fusion, and gather some of my own experiences in working with modern AI-driven sensor fusion techniques in the maritime and automotive industries.
Autonomous navigation, localization, and mapping mean the ability of the autonomous system to create a map of the surrounding environment, localize the machine on that map and make the navigation planning accordingly. These features are crucial in the development of autonomous machinery, vehicles, and vessels, and are often enabled by techniques such as sensor fusion.
Deep learning-based sensor fusion for situational awareness is a notably different approach from classic mathematical modeling. While the underlying core tasks of perception, prediction, and planning remain the same, deep learning tackles situational awareness in an integrated manner where all tasks are jointly considered and meaningful representations are directly learned from training data.
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