Topics: Situational awareness

Accelerating the development of future concept vehicles with AI-driven solutions and products

Silo AI, the largest private AI lab in the Nordics, is a trusted AI partner that brings competitive  advantage to product R&D. We co-develop AI-driven solutions together with our automotive customers to build the future concepts, such as next generation autonomous vehicles and mobility services. Based on the latest technology and increased amount of sensors and data, our recent engagements have covered deep-learning based sensor fusion, localization, multi-object tracking and situational awareness to mention a few. We’re looking forward to discussing how our 180+ AI experts can help you in becoming AI-driven at IAA Mobility 2021.

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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.

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Webinar recap: Silo AI x NVIDIA webinar – Situational awareness for vessels, Case: Groke Technologies

Groke Technologies is paving the way for autonomous navigation solutions in the marine industry. On June 2, 2021, we had the opportunity to learn about the future of the marine industry with Groke Technologies, a Finnish marine company. With the help of Silo AI experts and the use of NVIDIA’s products, Groke is utilizing sensor fusion technologies to achieve better situational awareness of a vessel’s surroundings.

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Sensor fusion

The how & why of sensor fusion

Sensor fusion in a nutshell is about combining the information from multiple different sensors in order to obtain a more accurate picture than any single sensor could provide by itself. From a theoretical point of view, sensor fusion is firmly based on understanding probability as a state of knowledge, which allows us to combine and manipulate different sources of information via the methods of Bayesian statistics. From a more practical point of view, real-time numerical sensor fusion is viable primarily due to the fact that Bayesian combinations of Gaussian probability densities yield Gaussian densities as a result. Thus if we can approximate our current state of knowledge and the incoming information using Gaussian probability densities, we can leverage analytical formulae and numerical linear algebra. This allows us to efficiently compute our updated state of knowledge.

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