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Comment by karol | Hacker News Reader

> The machine learning algorithms that underpin AEB systems have only been trained to recognize the rear of other vehicles, not profiles or other aspects.

Not something I have known about Tesla's autopilot before reading this.

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Well, it's a glorified cruise control, so it makes some sense, even if it's not optimal. My cruise control doesn't recognize profiles or other aspects either, nor does it the read of other vehicles, because it's an old dumb system. The last time I used a lane assist system (a few months back), it had a hard time even finding the lane. These are not self driving cars, these are driver assisting cars.

You should probably have a stronger sense of safety making a long trip with a 16 year old that just got their license driving. Even then, I wouldn't feel too comfortable taking my attention from the road for too long.

Geeze, this is terrifying given that my rear probably doesn't look like that of a vehicle when I'm walking, or cycling, or motorcycling, etc.

I've seen Autopilot 2 fail to notice cyclists. But then, it's designed for use on large, fast roads, rather than city streets.

I cycle through the streets of London every day, BTW, so I'm not suggesting that it's lack of recognition ability is a good thing.

That begs the question why machine learning is underpinning AEB in the first place. It seems like a textbook problem for distance sensors.

AEB = Automatic Emergency Braking. It is one part of the Autopilot system and doesn't apply to the front radar which recognizes the whole car.

At the time, I understand that Tesla was effectively relying almost entirely on the computer-vision AEB system to prevent crashes like this one in auto mode. The radar was only used to provide more information about the distance of objects that were seen and recognised by the single monochrome camera.

It makes you wonder what else we don't know about how specific and non-general the Autopilot is in its object recognition capabilities, doesn't it?

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