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New camera design can ID threats faster, using less memory

Image out the windshield of a car, with other vehicles highlighted by computer-generated brackets.

Enlarge (credit: Witthaya Prasongsin)

Elon Musk, back in October 2021, tweeted that β€œhumans drive with eyes and biological neural nets, so cameras and silicon neural nets are only way to achieve generalized solution to self-driving.” The problem with his logic has been that human eyes are way better than RGB cameras at detecting fast-moving objects and estimating distances. Our brains have also surpassed all artificial neural nets by a wide margin at general processing of visual inputs.

To bridge this gap, a team of scientists at the University of Zurich developed a new automotive object-detection system that brings digital camera performance that’s much closer to human eyes. β€œUnofficial sources say Tesla uses multiple Sony IMX490 cameras with 5.4-megapixel resolution that [capture] up to 45 frames per second, which translates to perceptual latency of 22 milliseconds. Comparing [these] cameras alone to our solution, we already see a 100-fold reduction in perceptual latency,” says Daniel Gehrig, a researcher at the University of Zurich and lead author of the study.

Replicating human vision

When a pedestrian suddenly jumps in front of your car, multiple things have to happen before a driver-assistance system initiates emergency braking. First, the pedestrian must be captured in images taken by a camera. The time this takes is called perceptual latencyβ€”it’s a delay between the existence of a visual stimuli and its appearance in the readout from a sensor. Then, the readout needs to get to a processing unit, which adds a network latency of around 4 milliseconds.

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