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The Camera Is the Lidar

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Re: The Camera Is the Lidar

#61

Post by nradov »

simonsarris wrote: post #56
amelius wrote: post #4

The problem is that deep learning should not be allowed in safety critical systems, because (1) the accuracy is always less than 100% even in known test situations, and (2) we don't know how it works and under what conditions it breaks down.

I was in a car crash two years ago where a man went into a diabetic fit/seizure and sped through an intersection, ultimately hitting a building and my car and killing himself in the process. It is too bad his car did not have some of this deep learning that is not 100% accurate. We don't know how humans work and under what conditions they break down, either.

There is no evidence that deep learning would give better performance than other collision avoidance algorithms in such a scenario.

Re: The Camera Is the Lidar

#62

Post by lucb1e »

nradov wrote: post #61
simonsarris wrote: post #56

Earlier quoted context omitted.

I was in a car crash two years ago where a man went into a diabetic fit/seizure and sped through an intersection, ultimately hitting a building and my car and killing himself in the process. It is too bad his car did not have some of this deep learning that is not 100% accurate. We don't know how humans work and under what conditions they break down, either.

There is no evidence that deep learning would give better performance than other collision avoidance algorithms in such a scenario.

But that doesn't mean we shouldn't try. I was agreeing with GP (u/amelius) because I had the same idea when reading the post, but the parent of your comment (u/simonsarris) makes a good point: we might not know deep learning as well as we might like to know it, given that it is being used in applications that have the potential to kill, but we also don't know our own brains that well.

Even if we don't understand deep learning to the degree that we would like, we can observe its safety record and compare it to humans'.

Re: The Camera Is the Lidar

#63

Post by lucb1e »

msadowski wrote: post #58
amelius wrote: post #4

The problem is that deep learning should not be allowed in safety critical systems, because (1) the accuracy is always less than 100% even in known test situations, and (2) we don't know how it works and under what conditions it breaks down.

Somewhat related anegdote: in France there one of the option for toll roads is a device that handles a payment for you and opens the barrier as the car approaches (it works up to 30 km/h). A friend of mine was doing that in a new car with some pedestrian detection system that decided to detect the barrier as a human and slam the brakes to the complete stop. From what I've heard it was not exactly pleasant.

So n=1 and it's not even the same technology.

Re: The Camera Is the Lidar

#64

Post by TheSpiceIsLife »

StavrosK wrote: post #46
patrickg_zill wrote: post #37

Earlier quoted context omitted.

The most general answer to your question is Peter Drucker's (famous management guru) observation that every doubling of production of an item (over the lifetime, not yearly) resulted in a cost reduction of 20-30%. So right now, with very few LIDARs produced, we have a high price, which will start dropping as more are produced. You might find this interesting: a single transistor used to sell for roughly the equivalen…

At quantity 1, though, most of the cost is from the person who has to package it. Qty 100 will give you a much more accurate price.

You can buy 9.6 billion transistors for AU$599 (US$430) in the form of an AMD Threadripper 12 Core 1920X.

That's AU$0.000,000,062,395,833 (US$0.000,000,044,791,667) per transistor;

Re: The Camera Is the Lidar

#65

Post by [deleted] »

nradov wrote: post #61
simonsarris wrote: post #56

Earlier quoted context omitted.

I was in a car crash two years ago where a man went into a diabetic fit/seizure and sped through an intersection, ultimately hitting a building and my car and killing himself in the process. It is too bad his car did not have some of this deep learning that is not 100% accurate. We don't know how humans work and under what conditions they break down, either.

There is no evidence that deep learning would give better performance than other collision avoidance algorithms in such a scenario.

[deleted]

Re: The Camera Is the Lidar

#66

Post by archi42 »

simonsarris wrote: post #56
amelius wrote: post #4

The problem is that deep learning should not be allowed in safety critical systems, because (1) the accuracy is always less than 100% even in known test situations, and (2) we don't know how it works and under what conditions it breaks down.

I was in a car crash two years ago where a man went into a diabetic fit/seizure and sped through an intersection, ultimately hitting a building and my car and killing himself in the process. It is too bad his car did not have some of this deep learning that is not 100% accurate. We don't know how humans work and under what conditions they break down, either.

I'm not in the market for a new car, but from what I read: There is something called "city safety" by Volvo, and I know that Mercedes has the similar tech (a friend learned that by not being run over by a distracted driver). So there are already technologies to prevent (or at least reduce the severity) of what happened to you (assuming he was below a certain speed threshold).

In constrast to the whole self driving stuff this DL is popular for: User input overrides DL input.

Re: The Camera Is the Lidar

#67

Post by jimduk »

deepnotderp wrote: post #31
flyinglizard wrote: post #27

Why is this better than separate LIDAR and camera? Because you're collecting NIR ambient light, your optics are wideband. Meaning that daylight would have a more pronounced negative effect on system range (easier to saturate the photocells). It's also low resolution (as most LIDARs are), and there is no color segmentation data. In an automotive application, I can't see a justification to unify both visual and LIDAR i…

It's better because there's no need for calibration, you always have perfect calibration. Solid state lidar has issues. The cofounder of Ouster, Angus Pacala previously cofounded Quanergy, a solid state lidar startup.

Agreed, spent time last year on a project fusing lidar and rotating LWIR (thermal) with some smart people and calibration took significant effort, mechanically, in electronic timing, and in algorithmic fusing. This looks like a nice step forward.

Re: The Camera Is the Lidar

#70

Post by eitland »

amelius wrote: post #12
barbegal wrote: post #11

Earlier quoted context omitted.

I think it should be allowed but the tests it should pass must be far more strenuous than for traditional software. I'm happy with failure rates of around 1 catastrophic failure every million hours. Even humans sometimes fail catastrophically and black out at the wheel for no detectable reason. That level of testing is well beyond what today's software and hardware is capable of. Waymo has to override their cars (dis…

And after certification, what if the company wants to push a quick update to all of its cars every now and then, through a remote update? Would that be allowed? How would we even know that it happens?

> And after certification, what if the company wants to push a quick update to all of its cars every now and then, through a remote update?

Late and a bit rough but here is my idea:

Install redundant self driving units in at least a good number number of the first few thousand cars in each generation.

When planning a release, push to the redundant unit in the cars already running in "production".

Use only primary unit as input to car as usual, but log the diffs between the new version and old version in the same way they now log driver intervention.

I think there is a number of issues this won't catch, off the top of my head what if the new self driving unit attempts to turn slightly faster on slippery road etc.

But it should be able to collect up realistic feedback really fast i.e. in a few months (crazy slow for modern application developers like me but more than fast enough for anything that should be allowed to drive unsupervised I guess :-)

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