Your vision is backed by a neural network with around 80 billion neurons implementing a general learning and intelligence model we don't have the first clue how to replicate. We're a bit further away from "Cameras working just as well as your vision" than I think you're anticipating.

> We're a bit further away from "Cameras working just as well as your vision" than I think you're anticipating.

Well surely that's what we should be testing. Is a self-driving car system safer than a human, or not? You're correct that self-driving car systems lack a "general learning and intelligence model," but they also have several obvious advantages over humans, like potentially better-positioned cameras, a more reliable "attention" system, faster reaction times, potentially more "experience" with different road conditions and scenarios, etc.

Also your eyes are far better at quickly adapting to things like wild changes in brightness and contrast than any camera

Cameras lack depth perception, which the brain very cunningly provides for humans at no additional cost.

Humans do get very clunky and slow when that depth perception is taken away. One example is night vision goggles which basically just project a flat screen onto your eyeballs. Many people report that they become susceptible to tripping, falling, hitting objects, or just straight up walking into trees with NVGs, in part because the loss of depth perception takes such a toll on their brain that they can't adapt in time. It takes hours if not days to fully adapt to NVGs, and even then you can't operate as quickly as you can in daylight unless you practice consistently (think SWAT or Navy Seals type training).

> Cameras lack depth perception,

Cameras lack any perception; perception is a function of brains.

But a single camera with appropriate processing can provide a wide array of depth cues (stereoscopy adds more.)

> Many people report that they become susceptible to tripping, falling, hitting objects, or just straight up walking into trees with NVGs, in part because the loss of depth perception takes such a toll on their brain that they can't adapt in time.

While loss of depth cues plays some role here, a much bigger factor, AIUI, is the narrow field of view. “Panoramic” NVGs have around 100° horizontal and 40° vertical field of view (non-panoramic NVGs often have around a 40° horizontal FoV), people have around (with eye but not head movement) a 210° horizontal and 120° vertical field of view. Looking directly ahead with even panoramic NVGs as you normally would without them, you literally cannot see lots of things, especially tripping hazards, and objects to your sides that you might laterally move into to avoid the things you can see in front of you.

Human binocular depth perception only works at distances of less than 30 feet, which is not very important for most driving. Everything beyond that is from other cues such as motion parallax, relative size, lighting and shading, and perspective.

The reason people become clumsy when using night vision devices is because they have a tiny field of view that only moves when your head moves. You can close one eye and walk around just fine. But if you attach two toilet paper tubes to your face, you'll have trouble balancing and avoiding obstacles. The clumsiness is caused by the lack of peripheral vision and the inability to quickly glance (since the only way to change what you see is to turn your head).

We also have a pretty large part of our brain dedicated to image recognition, in particular we are excellent at reconstructing a 3D environment from 2D images.

Furthermore, everything about the road is designed to help us humans. The size, color, placement, etc... of road signs and markings is specially designed for human brains to process.

As for the hardware, our eyes have very good resolution, field of view and an unmatched dynamic range. Far better than the stuff in Teslas.

So I'm not saying that it is impossible for computer vision to get away with just using cameras, but it is competing with what our brain does best. So a little help in a form of a LIDAR isn't too much IMHO.

It's a little strange to make a direct comparison between human cognition and computer vision given that the latter lacks the true ability to understand what it sees. You can train the computer for these edge cases, but humans largely do not have them in the first place because we're capable of formulating highly accurate understandings of what we see.

You can of course pick out discrete examples of humans failing tasks like these, but entire classes of computers routinely fail at things like this.

Humans have huge experiential neural networks behind their eyes that take context and knowledge of the real world into account. Teslas don't.