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Comment by gruturo | Hacker News Reader

Tesla will do self driving without a LIDAR, just cameras (ok, 8 of those), a RADAR and some ultrasonic distance sensors: https://electrek.co/2016/10/20/tesla-new-autopilot-hardware-...

Some people believe this to be impossible for the time being - LIDAR gives you a 3d point cloud (every obstacle, with its distance from you measured), which is amazing (but currently expensive, bulky, fragile, etc) while with cameras it's way harder, and RADAR can't see some materials at all.

Tesla argues that their suite is enough (humans manage with just 2 "cameras", some even 1), but the algorithms to let you safely entrust your life to such a sensor suite are harder (i.e. more far off in the future).

Kalanick + Levandowsky disagree with Musk, and were planning on calling him out on it.

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I know that you were just doing a tl;dr and not necessarily trying to start a discussion about it, but wow, that seems kinda stupid to be coming out of someone who is ostensibly fairly intelligent.

A 3D point cloud is great, but with the raw computing power we have these days, image processing should be pretty reliable, at least until LIDAR becomes more. . .well, reliable and affordable.

Doesn't the HoloLens just use an IR camera to map its environment in real time?

> A 3D point cloud is great, but with the raw computing power we have these days, image processing should be pretty reliable

In theory? Yes.

In practice? That's how the first autopilot fatality happened. The image processing confused the white side of a semi truck with the sky.

I thought that that was (also?) a problem with the radar, where it bounced under the truck and thought the road was clear.

> I thought that that was (also?) a problem with the radar, where it bounced under the truck and thought the road was clear.

Ultimately this was driver error, since he wasn't paying attention and didn't break.

As for why autopilot did not engage the breaks, you could blame any of the forward facing sensors since they all failed to see the truck.

If the vehicle had windshield-height radar, or a better vision system, perhaps the system would work.

The problem with the radar was it couldn't see the object at that height.

The vision system confused the trailer for an overhead road sign.

After the incident, I remember some people reporting more breaking occurring on highways underneath overhead signage. That made me think the fix they put in place was to raise the threshold for what is considered an overhead sign. That is, they chose to err on the side of assuming the sign is an object ahead.

That may have been a temporary fix. I'm only speculating based on driver reports that appeared in /r/teslamotors a month or two after the crash was reported.

I don't know whether Tesla ever gave an official statement about how they fixed that issue. Tesla wasn't found to be at fault so they probably didn't have to. Also, the whole system is constantly under development.

Part of the fix was to use radar: https://www.tesla.com/blog/upgrading-autopilot-seeing-world-...

Oh interesting. That's a pretty detailed report.

One thing jumps out at me,

> This is where fleet learning comes in handy. Initially, the vehicle fleet will take no action except to note the position of road signs, bridges and other stationary objects, mapping the world according to radar. The car computer will then silently compare when it would have braked to the driver action and upload that to the Tesla database. If several cars drive safely past a given radar object, whether Autopilot is turned on or off, then that object is added to the geocoded whitelist.

Relying on whitelists seems like a hack. Then again, I'm not building it =)

> Relying on whitelists seems like a hack. Then again, I'm not building it =)

Agree that it feels hacky at first, but once the whitelist dataset gets huge, it becomes unique training data for Tesla (and hopefully their machine learning will be able to generalize from it).

> Agree that it feels hacky at first, but once the whitelist dataset gets huge, it becomes unique training data for Tesla (and hopefully their machine learning will be able to generalize from it).

I guess. But then you still have to deal with rollout in other countries. And maintenance of sign locations which can change over time, get removed, etc. Still hacky IMHO.

How would machine learning make use of white-listed data? I doubt they could use that data to predict the GPS location of unknown signs.

If you mean image recognition, I assume if machine learning could properly identify the signs with accuracy, then they wouldn't need the whitelist. Then again, maybe they truly haven't collected a full overhead-sign dataset yet. I'd be shocked though if they don't by now. Anyway, you could be right. It would be fun to learn more about these setups.

True, but it was Mobileye _one_ quite low res BW camera system that decapitated the driver.

Yes the HoloLens uses IR to map it's environment, however if you move to an area that has a window it throws a caniption fit.

The reason I bring it up is that the HoloLens is pretty limited. Given what I've seen it do with its limited power and the kind of computing power they're putting in Teslas, not to mention the extra sensors (RADAR), it seems more than adequate to perform real-time image processing, even in the short term. That's not even considering what's coming down the pipeline, like fleet learning, the new Nvidia GPUs, etc.

Also when the HoloLens throws a fit it's maybe a 5% margin of error in terms of how the image moves. You'd think an autonomous vehicle wouldn't crash because of that much inaccuracy considering human drivers don't normally with less accuracy than that.

The Hololens uses an IR projection and camera in addition to 4 "environmental cameras", which is why it doesn't work well in very low or very bright lighting. It also uses the inertial sensors to help correct the map.

There are some things about SLAM that are best done optically.

The irony is a car with LIDAR on it won't sell, it looks ugly and too foreign. Tesla is designing with asthetics in mind.

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