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This Is How Driverless Cars Run With Their Own Visions. The Tools Are Unbelievable

The modern age of Information Technology (IT) and applied sciences enables everything to work fast and get organized. Before, marketing, logistics, entertainment, communication and transportation were only delivered manually. Most even have low trust and high doubts on technology use.

In the current millennium, there are suddenly high regards on its existence. 

One of information technology's appealing applications is transportation. Before, humans were already blown away by manufacturing automotive and higher forms of navigating systems. We have never thought of advancing even further. 

Not until these Cambridge researchers came into play. They were able to come up with a two-part software that will aid 'driverless cars' have a systematic vision while on the road. It's going to be a huge leap in the automotive industry! 

 

This Is How Driverless Cars Run With Their Own Visions. The Tools Are Unbelievable

This Is How Driverless Cars Run With Their Own Visions. The Tools Are Unbelievable

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  in Technology

Meet SegNet, the Online Artificial Intelligence's Vision Simulator

Meet SegNet, the Online Artificial Intelligence's Vision Simulator

In this online simulator, road views in images will be reassigned with a color-based label, so components will be emphasized.

Exhibit A: Central Park West

Exhibit A: Central Park West

This is how component sorting takes place. Elements were determined by color schemes. Those who have the access to the site can make this possible.

Exhibit B: Washington D.C.'s White House

Exhibit B: Washington D.C.'s White House

Categories in colors will be reflected finely according to the number of detected elements.

Second Supplementary System

Second Supplementary System

It is the one that will help determine user's location in real time; way better than the usual Global Positioning System (GPS). This was programmed by Cipolla and Kendall.

Exhibit C: New York Times Square

Exhibit C: New York Times Square

This image was deliberately labelled to enhance the way the assignment is done. Manual labeling also took place.

Exhibit D: New York Empire State Building Facade

Exhibit D: New York Empire State Building Facade

Minor issues were depicted from this image. Consistently, these errors are good feedback to further develop the accuracy of the pixel-base labeling.

Exhibit E: Golden Gate Bridge

Exhibit E: Golden Gate Bridge

With this kind of outcome, the image can be a basis to get rid of possible car-to-car-related commotions or accidents. This is awesome!

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