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Comment by verdverm | Hacker News Reader

hmm, I think the author missed that Waymo drove in snow 3 years ago [0], obviously outside of Pheonix. They also don't seem to understand it's CV + Lidar, not versus... I'd be surprised at a CV only system that can handle snow like seen in the Waymo example.

The rest of the article does not hold up once you realize the author is in an either-or mindset and thinks Waymo is not using CV, which they are, and has vertical vision stack, with arguably better experience at scaling ML

If you want to see actual FSD (without a human in the driver seat), this Waymo beta user has been video documenting on a regular basis: https://www.youtube.com/playlist?list=PL-13jt3ZPb7X6qJTo_MEn...

Having seen the "self-driving" displays from both, Waymo's shows Waymo information, which gives me greater trust in the system. By contrast, there are Tesla videos with some scary moments.

[0] https://www.engadget.com/2018-05-08-waymo-snow-navigation.ht...

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Yeah. The author here is just wrong.

Tesla's bet on CV is the biggest thing holding it back and everyone else is using both. Does Tesla have the best and most sophisticated CV tech? Probably. Is that better than having LIDAR and CV? Probably not.

If anything, the reason why Tesla's especially prone to Phantom of the ADAS is its reliance on CV and have no other inputs.

https://arstechnica.com/cars/2020/01/how-a-300-projector-can...

I agree with this. Humans have a tough time driving in the snow for quite a few reasons, but the ability of our eyes (which are fantastic technology) to differentiate objects covered in white snow is a big part of it, so camera's and CV are going to have a much tougher time that the combination of human brain and eye.

On the other hand, driving in the snow is a very very small portion of most driving commutes, so maybe the solution is for the car to automatically turn off autonomous driving in the snow. The warmer half the world would not care it does not work in the snow.

You can make cameras that can differentiate objects covered in white snow.

It's just going to be quite expensive, you would need quite good dynamic range and resolution, and I'd guess depth from de focus and parallax helps.

But that's with ~40 bits per pixel of dynamic range (17-18 effective bits per channel), at fairly high resolutions.

I'm a fan of both approaches, but Waymo is very heavily reliant on Lidar (and uses CV for augmenting). They have been working on this problem for 12 years now and have just launched in Phoenix, using high definition maps, lidar, and a suite of other sensors. They have <1000 vehicles. Zero fatalities attributed to Waymo.

Tesla has been working on this problem for somewhere close to half the time and with far fewer resources initially. Tesla has a million cars on the road across the entire world and only use basic sensors and CV. Six fatalities have been attributed to Tesla (and the driver).

So there really isn't a lot of data to go on for who is "winning" but Tesla has much more driver data. The fatalities are so low compared to the miles driven that it's difficult to really know if Waymo could achieve a better result.

Waymo is more reliant on lidar necessarily because lidar provides so much more data. You'd expect any algorithm to rely more on better data sources.

This isn't a counterargument, but sometimes more data isn't better data. I think lidar is super impressive in the right conditions, but debris in the air can be considered a bird even if it's a paper bag. If Waymo is having to still use CV to validate that it's a paper bag, then aren't they having to solve both problems? (Not that that is a bad thing, but it is at least two problems. I'm not certain who will achieve a working system, or if any of the players today will at all.)

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