this post was submitted on 02 Nov 2024
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The probe hones in on one of Tesla's most eyebrow-raising decisions when it comes to its driver assistance package: the insistence on exclusively relying on camera sensors instead of LiDAR and radar like its competitors, which CEO Elon Musk has long derided as a "crutch."

In 2022, the company went all-in on cameras, ditching ultrasonic sensors in its vehicles altogether — a decision that could prove to be a major mistake as it struggles to catch up with its competition and has now promised robust self-driving capabilities to owners who may lack the necessary sensor hardware.

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[–] DarkSurferZA 36 points 3 weeks ago (7 children)

This is one of the comments that Elon Musk uses a lot when he says humans drive with their eyes, but its untrue. We actually have a wide array of sensory systems that help us drive. Firstly, we use our ears, eyes and body motion to drive. Secondly, unlike a fixed camera mounted on a car, our heads are in constant motion. This means that we cover blind spots better than a fixed camera, and we are able to determine if it's a small deer really close by, and a large deer really far away. Our brains take multiple 3d images and stitch them together to determine size, distance and speed.

The best way to explain the driving using your eyes fallacy is basically to look at fpv RC cars, and see how much sensory information you have been robbed of while trying to pilot the vehicle

[–] [email protected] 6 points 3 weeks ago (4 children)

Not only are our heads in constant motion. Our eyes are also always in motion. We’re constantly, quickly and accurately shifting our attention to different points in our vision.

[–] [email protected] 5 points 3 weeks ago (3 children)

That's mostly accounting for the resolution and motion sensitivity in different parts of the eye. With enough cameras a car should be able too "see" more than we could at any one time.

[–] DarkSurferZA 7 points 3 weeks ago

No, not really true.

The way AI systems have been implemented in cars produces a flat image which we run through some fancy AI and the arrive at a conclusion. But what if 1 camera sees a child and for whatever reason, the other sees a clear road? The AI is not trained to process vision the way we do, where we use all our various senses including the conflicting info we get from each eye to arrive at a conclusion. It just does a merge and then process. It should process from each sensor, then reprocess to arrive at a conclusion

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