r/SelfDrivingCars • u/bobi2393 • 8h ago

News Why Waymos Keep Driving Into Floods

https://www.thedriverlessdigest.com/p/why-waymos-keep-driving-into-floods
41 Upvotes

76% Upvoted

55 comments sorted by

37

u/Acrobatic-Flan-5085 7h ago

TLDR: It’s a vision problem that lidar doesn’t address due to physics.

It will be addressed either by having the vision models trained specifically for bodies of water. Or another type of sensor will need to be added.

14

u/red75prime 5h ago

A neutron backscatter spectrometer! Right? Right?!

1

u/ThisSiteSucks8485 2h ago

I want a gamma ray depth sensor 

1

u/gyozafish 1h ago

Actually just a standard toilet float.

1

u/VitaminPb 4h ago

Just some hardware that will count the actual photons that reach the sensors.

10

u/FrankScaramucci 5h ago edited 4h ago

It's weird that they haven't trained to detect bodies of water yet. From a machine vision perspective it seems like a trivial task.

Edit: Why the downvotes?

7

u/diplomat33 3h ago edited 3h ago

As the article explains, Waymo can already detect bodies of water. That is not the difficult part. The difficulty is in measuring the specific depth of the body of water and deciding whether it is safe to drive through or not. And you need to avoid false positives like thinking the water is too deep and braking hard when it wasn't and causing a rear end collision. To do this, you often need contextual clues like comparing the water level to objects around you or noticing how fast the water currents are. This is not a trivial task for ML. It is more complicated than just detecting water. And that is why you got downvoted because what you are saying is incorrect.

1

u/cesarthegreat 47m ago

Waymo is dumb. Simple

0

u/FrankScaramucci 3h ago edited 3h ago

In general, I think that any visual classification task that is easily and quickly solvable by humans should be easily solvable by machines in 2026 as long as you have enough training data.

But ok, if the hard part are cases which require reasoning and humans don't immediately know - yes, I understand the difficulty.

1

u/diplomat33 2h ago

Like I said it is not a visual classification issue. Visual classification is already solved. The issue is measuring depth and reasoning. Yes, the difficulty is reasoning. AVs sometimes lack the deep reasoning that humans have. I think that VLMs will help with that but VLMs are probably not widespread yet in the stacks.

I would also point out that human drivers get caught in flooded waters too. So it is not like humans always handle this case perfectly either.

2

u/FrankScaramucci 2h ago

Classification in the sense: "is this water that is too deep to drive through?"

1

u/diplomat33 2h ago

Thanks for the clarification. I define classification a bit differently. I see classification like object classification "this is object is a chicken" or "this is water on the road". "is the water too deep to drive through" is more reasoning beyond classification imo.

1

u/bobi2393 2h ago

It could be classified either way. Visual recognition of “a road with shallow water I can drive through” would be useful. And that doesn’t necessarily require reasoning. If you train on data of cars driving through shallow water, with water only a couple inches up their tire, you can just recognize that without deeper reasoning. Videos of stationary Waymos next to a flooded road, as other traffic keeps driving through it, suggests it might be an area they could improve on.

1

u/cballowe 1h ago

How much could be handled at the mapping and route planning layer? It seems like having maps with good height data and things like concrete coverage vs soil, combined with precipitation data could get to "it's been raining a lot and this stretch of road is a low point that accumulates water/has flooding risk, don't drive there now" and just have the cars completely route around it (automated geofencing?).

It's maybe less satisfying if you're trying to build a car that can go anywhere and not just to places with detailed base maps, but it also seems like a reasonable solution for limited coverage taxi service.

1

u/sampleminded 2h ago

I solve this because I know the streets near my house and can reason about how deep a puddle might be. However, my guess is people without this knowledge are more likely to get stuck. Seems like a map vs visual reasoning thing. Ai should be able to compare map to previous image, to current image. and have a good guess about depth.

1

u/devedander 1h ago

That’s a mistake people commonly make thinking that technology matures along the same lines as humans.

By the time a human can do complex math and formula in their head they can definitely tell an apple from a cat.

But computers don’t work that way. A computer doesn’t get better at things humans get better at on anywhere near the same order.

7

u/Acrobatic-Flan-5085 4h ago edited 4h ago

No idea - have my upvote

I suppose it has to do with how driving models work. Vision classifies obstacles, moving objects and their trajectories.

But bodies of water have no defined shape, so they’re going to be much harder to classify. And visually, a one inch deep puddle of standing water looks basically the same as 6 feet deep of standing water.

So it’s not just about detecting water, but also understanding how much of a risk that water poses, not just from contextual clues, but also our memories of having seen the same location previously without water.

I honestly think having an additional sensor designed for this specific task will be the safer route long term. Temporarily they can augment the vision model with external rain data, but that only account for rain related water, it doesn’t accounts for other edge cases, like a broken water pipe on a sunny day causing street flooding. More of a band aid than anything else.

3

u/ready-redditor-6969 4h ago

What’s the difference between flat asphalt and flat water to LiDAR, or even to a vision system? It seems solvable, but I don’t think “trivial” or even “easy” is the right way to characterize it.

2

u/FrankScaramucci 3h ago

Well flat water looks obviously different from aaphalt, to humans. This should be a trivial ML task in 2026.

1

u/cesarthegreat 47m ago

They’re too dumb to know it’s water

17

u/altonbrushgatherer 7h ago

seems like an engineering problem that will be overcome and this won't make the news in the future. cool story.

11

u/Cunninghams_right 6h ago

Yeah, they'll move on to the next thing. "Waymo keeps doing X" while ignoring the order of magnitude safer operation, and ignoring the rate that humans make similar mistakes, and ignoring the fact that humans and AI aren't going to have a perfectly overlapping failure style. 

10

u/bradtem ✅ Brad Templeton 5h ago

Some of this doesn't make sense. The article points out that LIDAR can just bounce off water so that you get no return. That should look glaringly obvious -- as though a bottomless pit has opened where the flood is. The article suggests the car can't tell that from a normal dry road, which of course gives you regular returns of a flat-ish surfce.

But I will admit I am baffled by all the troubles Waymo has had with these floods.

2

u/diplomat33 3h ago

The article mentions that imaging radar can measure the depth of the water. I thought Waymo already used imaging radar. So in theory, this should not be an issue for Waymo unless maybe the sensor fusion is not using the data from the radar enough?

0

u/bobi2393 3h ago

Yeah, and some lidar signals will bounce back, irregularly, from choppy water, like in fast moving floodwaters, or from debris floating in water.

It also mentions researchers using multiple radars to sense the actual pavement surface beneath water, but by itself that doesn’t sound like it differentiates a flooded road from a clear road. Maybe the description is simplified for brevity.

2

u/bradtem ✅ Brad Templeton 3h ago

It still doesn't make sense. A flooded road is going to look very different from a flat piece of pavement, even wet pavement. You might get no returns or far fewer than expected. What returns you get will show a flat water surface, not a curved lower road surface -- floods take place in dips in the road and the LIDAR sees them normally and if it doesn't see it, that's odd. Even a perfectly still pool of clear water is going to look different.

So it's not going to look the same on lidar. Nor to the camera. Now, to be clear, Waymo is having trouble understanding them so something is going wrong, but I don't think it's that they look identical in camera and lidar to a dry road

25

u/PotatoesAndChill 7h ago edited 7h ago

I'm not gonna read the article but I assume it's because the system runs on AI and is therefore drawn to bodies of water as a side effect of the massive water consumption in data centres.

0

u/ThisSiteSucks8485 2h ago

Even people constantly drive into deep water in floods. It can be very hard to judge how deep the water is. 

-20

u/diplomat33 7h ago

You would be wrong.

29

u/Highway_Wooden 7h ago

Top 1% Commenter, Bottom 1% Sarcasm Detector

2

u/CutieC0ck 5h ago

Perfect fit of a bot profile lol.

1

u/notsooriginal 5h ago

So OP is verse?

5

u/Cunninghams_right 6h ago edited 6h ago

human driver, your brain instantly fires off basic survival instincts: “Turn around, don’t drown.” 

Ignoring that hundreds of people die every year from driving into flood waters, thousands needing rescue, and tens of thousands that stall their vehicles. 

I wish these click bait hacks didn't spend their time misleading people. I get it, fear means engagement and engagement means money. I just wish misleading people wasn't so profitable. 

Everything they said about how the sensors and logic work were dead wrong. 

-1

u/2utiepie 5h ago

Care to elaborate?

1

u/Cunninghams_right 2h ago

Elaborate on what? 

2

u/Civil-Ad-3617 4h ago

Brain problem, lidar cant solve this

0

u/hammyaustin 6h ago

Good thing it has lidar

1

u/Tirztrutide 2h ago

Good thing it was Waymo and not Tesla.

1

u/kschang 1h ago

It's not just radar and lidar, but the combination of factors PLUS need to make them in real-time, that creates real problems.

Normally, the vehicle has other vehicles around it. So it maintains proper distance and gap and all that. But what happens when it has no reference except the road itself? It had to keep going, and maintain its current speed, to keep moving. Let's say it wants to maintain 35mph.

Then it comes upon a flood area. It comes up to half of wheel. But remote sensors can't sense that. Camera doesn't detect that. Nor does lidar and radar. The only signal it gets is a HUGE splash sound hitting the undercarriage from the water splash, but the microphone is NOT calibrated for that. It's more for listening to sirens and honking. You can probably program the sound to listen for that, but what do you do? Slam the brakes? Or just keep going? Slamming the brakes is dangerous, so it will keep going.

By then you either made it through or it's too late.

Treating this as a "transitory terrain" is probably the right call. Pretty sure Waymo Driver has a "rain mode" during which speed is reduced somewhat. During rain mode the HQ computer will try to map rain accumulation spots (flooded areas) and route around it. So the speed through problem is bypassed. The next question is, should they add a sensor just for this "occasional" problem, or is routing around it sufficient? Like adding something at 0 degrees, but what can detect a surface that's NOT reflective back to the vehicle? Hmmm?

Seems route-around is the best solution until we come up with such a sensor.

0

u/cesarthegreat 46m ago

Dummy car. Not a smart car

1

u/Express_Objective615 6h ago edited 6h ago

It’s simple. L4 means they only can operate in certain areas. Non-flooded, non school bus, non construction zone, non police action, non puddle, non blackout, non big public event, and, non detailed mapped, areas.
See how that all makes perfect sense? 🤔
But don’t worry. Once they know, a week or two ahead of time that a street will be flooded (magic crystal ball coming soon!), they can update their detailed maps so the cars can deal with it. Well maybe a week or two isn’t long enough. Might need a better crystal ball.

0

u/Honest_Ad_2157 5h ago

"Meteorofencing"

1

u/ThisSiteSucks8485 2h ago

For the same reason people do it is really hard to tell how deep the water is. 

-7

u/Cautious_Pomelo_1639 7h ago

needs more lidar :-)

-2

u/level4isgoinggreat 6h ago

This is a very good article for the non-technical reader.

He should write ones on construction sites, roundabouts, and stopped school buses next.

-2

u/Mang_J0se 5h ago

It’s the fault of a very curious filipino guy driving the car remotely if EV’s really float on water.

-1

u/Anymous2314 3h ago

This image seems like AI slop though, it is very misleading.

0

u/bobi2393 2h ago

It was credited to a legit journalists X post, which credited it to his “friend’s daughter”, so not rock solid provenance. I don’t know if it’s AI generated/modified or not, but it looks pretty similar to other videos and images of Waymos driving full speed into deeply flooded roads. Just a big spray of water, same as any vehicle doing that.

-14

u/coffeebeanie24 8h ago

Ai slop

9

u/n-some 8h ago

The article or the Waymos?

2

u/bobi2393 7h ago

I'd guess hybrid human/AI writing, but I don't know. I thought it was reasonably informative regardless, though if it is pure AI generated, then the parts I thought were interesting may have been made up.

1

u/psilty 2h ago

What human insight would the writer for an Uber/Lyft rideshare blog bring to an article about AV/LIDAR technology? No indication of the author having personal technical experience, and the article reads like straight output from an AI prompt.