Sensor fusion for autonomous machinery

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.

Silo AI is expert in working with

Autonomous vehicles

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.

Autonomous vessels

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.

Autonomous machinery

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.

eBook: Towards autonomous machinery – Situational awareness enabled by sensor fusion

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.

Towards autonomous operations

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.

Our expertise

Getting things done autonomously requires combining many fields of expertise, investigating thoroughly what is possible and how to build it, and handling the complexity of all the interlinked subproblems. Silo AI’s 220+ experts with 100+ PhDs can help you with:
  • Planning and implementing interplay of several sensors
  • Leveraging sophisticated deep learning as well as mathematical modeling to achieve the highest accuracy of data
  • Building a robust Machine Learning Operations (MLOps) infrastructure to get full transparency for the machine learning training workflows
  • Evaluating and tailoring the AI models and algorithms for specific needs and use cases
  • Deploying AI/ML either on the device level, EDGE, or cloud
  • Human in the loop – integration to your current processes, environments, and automation systems
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Sensor fusion for situational awareness

Juha Rokka

CEO, Groke Technologies

Asier Arranz

Developer Marketing, NVIDIA

Oscar Guerra

Deep Learning Start-up Account Manager, NER at NVIDIA

Jesus Carabano Bravo

PhD, Senior AI Scientist, Silo AI

LEARN WITH SILO AI, WEBINAR
LEARN WITH SILO AI, WEBINAR

Watch the webinar recording

Sensor fusion for situational awareness

Juha Rokka

CEO, Groke Technologies

Asier Arranz

Developer Marketing, NVIDIA

Oscar Guerra

Deep Learning Start-up Account Manager, NER at NVIDIA

Jesus Carabano Bravo

PhD, Senior AI Scientist, Silo AI

Sensor fusion articles written by Silo AI experts

ARTICLE

Real-world applications for sensor fusion: situational awareness for autonomous machinery

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.

ARTICLE

Towards autonomous navigation, localization, and mapping

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.

ARTICLE

Core technical principles for fusing sensor data with deep learning

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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