JV Utilises Deep Learning & Edge Computing To Make Factories Smart

Hitachi is aiming to utilize deep learning and edge computing technologies to make machines intelligent, in order to improve productivity. In pursuit of this, it has formed an automation joint venture with industrial robot and factory automation company Fanuc and AI-startup Preferred Networks.

Intelligent Edge System will utilize AI technologies in the social and industrial infrastructure field. It will develop fast, real-time control systems for network-connected industrial robots and machine tools. These control systems will leverage deep learning AI technology to become smarter over time as linked machines manufacture products.

Preferred Networks will use its deep learning AI technology to process information more efficiently and speed up data analysis. This is hoped to boost production line productivity and allow robots to recognize things and adjust their moves accordingly. Robots will also be able to automatically take on the task of an adjacent robot on the production line in case it breaks down.

Edge computing will help the initiative by handling the task at the edge of the network instead of centrally processing data. This will let machines on the production line process the massive amount of data, such as the movement of mechanical hands, on the spot.

Preferred Networks has already applied its AI expertise for Toyota Motor and Nippon Telegraph & Telephone. Toyota Motor invested in the startup for the development of autonomous vehicles that can learn various driving conditions by processing data by themselves rather than relying on cloud computing.


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Internet Of Things | IoT India

HERE utilises connected car data to ‘supercharge’ its Real-Time Traffic service

HERE technologies has announced its next-generation Real-Time Traffic service which utilises connected car data to improve the accuracy of traffic conditions.

Live data is fed into HERE’s service from sensors built into compatible Audi, BMW, and Mercedes-Benz vehicles with traffic probe information. The company says the use of the sensor data provides ‘significant’ improvements to its traffic flow data, especially on arterial roads.

“This is the world’s first traffic service to aggregate live rich vehicle sensor data from competing car brands and it represents a major step by HERE to make driving safer and more efficient for people everywhere,” said Ralf Herrtwich, Senior Vice President Automotive at HERE Technologies. “While it helps drivers making informed decisions behind the wheel today, it also moves us closer to realizing our vision of a live representation of the road environment needed for both advanced driver assistance systems (ADAS) and self-driving applications.”

HERE’s improved Real-Time Traffic service is available to all current and future customers from any industry. The traffic data covers more than 60 countries, while incident features such as Traffic Safety Warning are available in more than 30 of those countries.

Using the braking sensors on equipped vehicles, the company has improved features such as its Traffic Safety Warning system by detecting when vehicles are braking hard to ensure other drivers receive timely notifications to be aware.

“Traffic information providers often define their capabilities by the number of probes they collect data from, but data richness will increasingly become the defining factor between a good service and an excellent one,” comments Andrew Hart, Director at SBD Labs. “The dozens of sensors equipped on modern cars make them the richest possible source of real-time traffic data. HERE has developed a win-win approach to accessing and analyzing rich vehicle data.“

The enhanced HERE Real-Time Traffic is the first of the four vehicle-sourced data services HERE announced last autumn.

You can find out more information about HERE Real-Time Traffic in a blog post here.

Are you impressed with HERE’s use of connected car sensor data to improve real-time traffic information? Let us know in the comments.

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