Global Technology - June 2020

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What new technologies will dramatically transform your world?  We’ll present an exclusive preview of the stunning breakthroughs emerging from the world’s leading research labs. 

Research presented June 16 at the Conference on Computer Vision and Pattern Recognition (or CVPR), described a system that is able to distinguish objects including cars, bicyclists, and pedestrians and gauge their direction and oncoming speed, even around corners. This approach allows for collision warning for pedestrians and cyclists in real-world autonomous driving scenarios — before seeing them with existing direct line-of-sight sensors.

In earlier research, the Princeton-based team used light to see objects hidden around corners. But those efforts are currently not practical for use in cars both because they require high-powered lasers and because they are restricted to short ranges.

While conducting that earlier research, the team wondered whether it would be possible to create a system to detect hazards out of the car’s line of sight using imaging radar instead of visible light. The signal loss at smooth surfaces is much lower for radar systems, and radar is a proven technology for tracking objects. The challenge is that radar’s spatial resolution — used for picturing objects around corners such as cars and bikes — is relatively low. But fortunately, the researchers created algorithms to interpret the radar data allowing the sensors to function.

The algorithms that they developed are highly efficient and fit on current generation automotive hardware systems.  So, we might see this technology in the next generation of vehicles.

To allow the system to distinguish objects, the team processed a part of the radar signal that standard radars consider background noise rather than useful information. The team applied artificial intelligence techniques to refine the processing and read the images. To do this, the computer running the system had to learn to recognize cyclists and pedestrians from a very sparse amount of data.

First, it must detect if something is there. If there is something there, it asks “is it important?” That is, “Is it a cyclist or a pedestrian?”  Then it must locate it.


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