Presumably they haven't had the chance to do a lot of flood training but now they have that chance.
The huge advantage they have over people in general is that ideally if they figure this out then it will stay figured out. Then they can slowly role out and watch for the next hitches from new situations.
https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-f...
You may be relieved to hear Wayno is rolling out to Portland, Oregon. It's not in the south, and with over 150 rainy days per year, it ranks among the rainiest US cities.
I guess water propulsion... and a rudder?
I can appreciate the cameras and lidar on the Weymos don't give their remote operators a lot of good data about the depth of water on the road-way. As you point out, humans in cars often don't get this right. I think the humans that don't drive into deep water are the ones who a) give any amount of water on the roadway a big NOPE and b) people familiar with the local environment and use multiple visual clues to judge the true depth of the flooding.
Then ask the human.
I'm not sure you'd walk away the idea that they have equivalent intelligence. The human at least knew the water was there and took a risk, the car, presumably, had no idea what was in front of it and drove into it anyways.
Why can't Waymo ALSO develop the same smarts and just also solve the sensor fusion issue such that they can use the right set of sensors in the right environmental conditions, and then leapfrog Tesla's capabilities?
You can have even more intelligence with both.
People drive into floods too. They just don't get sensational articles written about it, just posted on reddit.
Tesla failed to deliver driverless cars but now is pivoting to the much more complex fully autonomous robots. And we can’t get AI to stop hallucinating facts, but any day we are going to be at AGI in a few years? I get people want these things to happen, but I just don’t see it happening any time soon. The whole tech industry feels built on what maybe, someday, possibly, could happen but most likely won’t, but we are all going to act like is a sure thing and is just around the corner.
Are there no responsible adults left at these tech companies?
My understanding was that ICE cars have trouble because water get's drawn into the engine. Water in the engine causes it to stall. And the engine must have air in flow and out flow.
An electric car doesn't need air in the same way (no oxygen to ignite with gasoline, no air to compress and expand).
Shouldn't electric cars to much better at driving through water?
They can also float just like a regular car.
These self-driving companies have made very little progress on dealing with weather for how long they’ve spent on the problem.
Also, the drivers in Miami are a bit more unpredictable than the average driver around the country in my experience, so good challenge cases for self-driving development.
Many many years ago I happened to be in a conversation with one of the guys on a team that participated in the 2005 DARPA Grand Challenge. It was only the second such race after the 2004 one, but arguably the one which set off the autonomous driving race we see today. (Sebastian Thrun's team came in 2nd.)
I went into the conversation thinking it was going to be an extremely challenging but tractable sensors + control-systems problem. But by the end of the conversation I was like, OMG this is going to be a long-haul slog of solving an unending stream of problems, some potentially even AI-complete (i.e. requiring human-level judgment.)
We mostly discussed why his and most other teams failed and the failures were so myriad and so technically intractable that I could not see a path to full self-driving for at least two decades. And all of this was offroad, so it didn't even approach the challenges of sharing human-occupied streets. I cannot remember any details unfortunately, but I remember that one car got stuck in a loop due to a problem that would have been trivial for a human to bypass... but that required human-level judgment. As an analogy it was something like a soft obstacle that could safely be driven over. But for the car to know that it would require a database and an "understanding" of all possible obstacles. An LLM could have helped, but back then they were still firmly in the realm of SciFi.
So the only feasible solution was to painstakingly identify all the edge-cases and work through them slowly, carefully, one-by-one. Which is what Waymo has been doing. This is also why when Elon made his "full self-"driving announcements I knew he had absolutely NO idea what he was talking about, and he was likely going to move fast and break people.
Flooded streets is just another "bump on the road" to full self-driving, but it seems we're actually getting there now. In retrospect, my 2-decade estimate was surprisingly accurate, I have no idea how I landed on that particular number!
I think that self driving cars won't ever be able to handle every condition out there, and so there's probably a time when the system will be paused / shutdown when conditions aren't safe to drive in. Honestly, I wish we could do this with human drivers for that matter, too, but some will press on even when they shouldn't...
A closer analogy would be ""Chicago O'Hare pauses flight departures due to a winter storm after 3 planes slide off the runway due to ice"
Absolutely I think there will be a disconnect between when people think they should be able to drive somewhere (ie to work in a no-visibility blizzard) and when ideal self-driving cars would allow themselves to operate. Maybe society will adjust to be more flexible to natural conditions, or maybe people will get frustrated and drive themselves into the poor conditions as always.
given accurate mapping + realtime imaging, this should be possible albeit a Big Project(tm).
I don't think they're barreling into foot+ deep water.
I think they're driving into shallower "perfectly navigable but still deep" puddles at normal for the roads speed and this pizza delivery boy type behavior is making passengers clutch their pearls because they are expecting their robotaxi to drive like a high end chauffeur.
> It follows an incident on 20 April in San Antonio, Texas, where an empty Waymo vehicle entered a flooded road and was swept into a creek.
Nobody in it but sounds serious enough.
We're contemplating standing up an EV shuttle service in Oak Park. It will fail. As I understand it, we've piloted non-EV versions of a shuttle service; they failed. The problem is that in small local areas, the staffing for a useful transit service is too expensive; that's because "useful" imposes constraints about responsiveness, coverage, and most of all hours of service, which mean the service won't pencil out with the ridership it'll get.
An autonomous vehicle transit service in our muni would probably work fine; it's a strict grid system with very low speed limits (AVs will, in our area, be strictly better drivers than the median human drivers --- this isn't a statement about human fallibility so much as an observation about scofflawry in our area). And if the product existed, we could afford it, because we wouldn't be paying fully loaded headcount costs for 2+ shifts of drivers at epsilon levels of utilization.
For whatever it's worth, I don't really have "autonomous vehicles" and "LLMs" in the same bucket in my head. I'm bullish on both, but for very different reasons. It usually doesn't occur to me to think of Waymos as "AI", though, obviously, they are.
I'm pretty conservative about this stuff but the waymo is genuinely nice to ride in.
A car that only fails in a road conditions edge case is good enough for the vast majority of cases. You accept that, and issue a manual override for when that edge case pops up. Then you add that edge case to your training sets. Then the issue never comes up again.
If you think that "flooded roadway" is a case that's handled gracefully by every human driver, and it's the AI that's uniquely prone to failure, I have news for you.
Multiple cities with uncommonly flooded roadways get surges of "water flood engine damage" cars at the repair shops in the wake of extreme weather events. Human drivers underestimate just how flooded a roadway is, try to push through it, and have their car choke, die, and float there, waiting for some good samarithan with a snorkel and a long rope to pull it out. Then someone gets to play the fun game of "is this ICE toast or will it run once you get the water out".
A self-driving car AI pays less attention than a human driver at his best. It isn't as aware as a human driver at his best. It doesn't have the spatial reasoning, the intuitive understanding of physics and road dynamics that matches that of a human driver at his best.
Human drivers still fall behind statistically, because human drivers are rarely at their best. And the worst of human drivers? It's really, really bad.
AI is flawed, but a car autopilot doesn't get behind the wheel after 3 beers and a pill of benadryl. It doesn't get tired, doesn't get impaired, doesn't lose sleep or succumb to road rage. It always performs the same.
Until it gets a software update, that is. The road performance of an average car AI only ever goes up. I don't think that's true for human drivers, frankly.
I live in NYC now. Drivers here are some combination of utterly selfish and mindlessly distracted. You can't even trust them to stop at red lights. It gives me a huge amount of pause riding here.
"Cars are dangerous, necessary in many places, but often driven by irresponsible people" is a huge problem that needs solving. Waymo seems to have been doing a pretty fantastic job at it.
And even if they couldn't figure out how to route around floods, floods are rare. They're still a net benefit to society.
To your point, knowledge work, _as a whole_ is a much larger and complex domain than self-driving.
That said, I know a scenario like that would never happen, probably for the best.
I've never lived in a hurricane area, but when I think of news coverage of problematic evacuations, they're showing people stuck on highways, not people stuck in urban traffic grids.
It's a throughput problem. Computer controlled "car trains" with shorter following distances can boost traffic throughput, but I don't think that would be enough to make evacuation of large cities actually feasible. The highway system is simply not built for that use case. Especially since evacuation often occurs during inclement weather that reduces capacity.
AFAIK, most places try to figure out how to make shelter in place work, because mass evacuation is likely to end up with many people facing the weather event while on the highway.
You could theoretically do better with busses and trains, things, but there's likely not enough busses that are setup for long distance travel available: lots of municipal bus fleets are setup for alternate fuels which is great for emissions but makes it hard to travel to a neighboring state, because there may not be appropriate fueling opportunities on the way. Etc, etc.
There might be some level of adoption where they would, but honestly we're back to "but what about trains/trucks?".
Half the problem with evacuations is people don't want to leave behind their stuff to get destroyed. You'd basically be better off getting a fleet of semi's with some quick and dirty cube system thrown up than a bunch of automated sedans.
This is a big assumption.
This requires that all cars are self-driving cars capable of complex reasoning on in-car compute without relying on network connection, as network connections can't be assumed reliable in hurricane conditions.
At which point we've reinvented privatized buses with a last mile convenience vs greatly reduced throughput trade-off.
I agree, but there are a number of people here in Florida who will do it or die trying (emphasis on the die trying)
You could maybe use short-wave infrared cameras combined with ground penetrating radar, but it'll get real expensive so probably not commercially viable.
I think the only "good" solution is to have the car be overly paranoid, and if it detects water on the roadway that's bigger than some arbitrary diameter (to rule out mud puddles), then the car has to assume its a flood, stop, and escalate to a human or change the route.
Alternatively, just don't run Waymo operations during flood/flash flood warnings. Maybe we as a society need to top forcing everything to still operate normally during natural disasters. It's OK to shut things down when safety calls for it, and that applies to human drivers too. If areas are flooding, stay home.
FTA
> the company said that it shipped an update to its fleet that placed “restrictions at times and in locations where there is an elevated risk of encountering a flooded, higher-speed roadway,”
> But even those precautions apparently were not enough to stop the Waymo robotaxi from entering the flooded intersection in Atlanta. Waymo told TechCrunch on Thursday that the storm in Atlanta produced so much rainfall that flooding was happening before the National Weather Service had issued a flash flood warning, watch, or advisory.
- Find the edge of the water using vision or lidar
- look up the ground height at that position in your map data. That is the water level
- run a flood fill of the local 3d map starting from that point, with that water level. That gives you an exact shape of the puddle
- for any point on your planned path, you can now check if the point is in the puddle (per the flood fill above) and how deep the water is (difference between puddle's water level and ground height)
- use that either as a go/no-go for a planned path, or even feed this into your pathfinding to find a path with acceptable water level
The main limitation is that it assumes that the ground hasn't changed. It won't help in a landslide, or on muddy ground where other cars have disturbed the ground. But for the classic case of the flooded underpass or flooded dip in the road it should be very accurate
If the apparent road surface is higher than the mapped ground surface, probably a puddle. If your point cloud has a big hole, also probably a puddle.
This assumes you aren't doing ground plane removal, of course. But it's quite likely that Waymo is using a heavily ML approach these days, and I can imagine the poor thing getting very confused if it's not an explicit training goal.
If you can’t handle this issue, you really can’t operate in Atlanta.
It’s 2026 and self-driving cars can’t tell the difference between a puddle and a flooded street, something a 3 year old can do.
Google literally just got off stage telling us that AGI is almost here. Wake me up when this doesn’t feel like an NFT ape fever dream.
And here we are talking about this like “oh gosh golly I wonder if this is some simple thing that could have been easily solved but they were trying to avoid regressions”
Get out of town, man.
I wish every dollar spent by investors on Waymo went into more frequent public bus service instead. A regular-ass bus with a human driver.
Ideally, driverless cars will one day be better drivers than humans and this will save tens of thousands of traffic deaths per year. Holding up progress because cars will be confused in extremely rare or improbable situations will cost more lives than it saves.
Random planters in the middle of the road? Streets that narrow and then widen? Drivers start slowly creeping along, which means they are less likely to injury pedestrians.
maybe a little biological brain engineered to think it is a car with api access to the car hardware via the llm?
imagine you get into the car and in the center console you just see a floating brain in vat like fallout
The LLM will apply the high level reasoning needed to deal with longer time horizons and complex decisions, like deciding that the best way to reach the car wash 100 yards away is by walking.
The thing about weather is that with a fully automated fleet they can just stop and give up on driving instantly. Rain in Miami doesn’t tend to last very long except in specific storms like hurricanes. Waymo can just not operate during those times.
I’m very doubtful that a lot of these inherent problems with the technology are being rapidly solved. See: the article.
The question is: why haven't you fixed this already?
So it's actually entirely rational that the bar for companies to be able to ship software that makes those fatal errors without consequence other than an insurance payout should be higher (especially since when fatal error rates can only be estimated accurately over the order of millions of miles, driverless systems are more prone to systematic error or regression bugs than the equivalent sized set of human drivers, and the cost and appeal of autonomy probably means more experienced drivers get replaced first and more journeys get taken)
Because this part is really hard, and that's why Tesla abandoned the fusion approach. You cannot possibly foresee all the conditions in which LIDAR or any active sensor will malfunction/return wrong data/return data that's only slightly off for that ONE specific time. And even if it doesn't, you need to trust it to not return noise. And when it does return noise, how do you classify it as noise?
Cameras are passive sensors - they get whatever light comes in and turn it into an image. Camera is capturing shapes that make sense to the neural nets: it's working. See all black/white/red/cannot see any shapes? Camera is not working, exclude it from the currently used set of sensors or weigh it less when applying decisions, because it's returning no signal (and yes, neural nets have their own set of problems).
EDIT: cameras also provide more continuous context: if 1 pixel is off, is clearly bright red in a mostly-green scene where no poles can be identified, the neural net will average it out and discard it as noise. If 1 pixel says "object" in LIDAR, do you trust it to be correct? Perhaps the ray just hit a bird or a fly, but you only see a point, it's a lossy summary of the information you need.
As is, Waymo's playing it smarter than Cruise did, but they're not all in on AI yet. So I don't expect them to "leapfrog Tesla" in that dimension - and it's the key dimension to self-driving.
Tesla trains it models from actual drivers purely based on (input) Vision and (output) actuators - Brake, Steering, Accelerators.
Human output is based on what they and the camera sees. So, it's a 1:1 match.
If Waymo were to do that, it'll muddle the training set. The Lidar input may override camera input.
I always struggled when Musk mentioned Lidar will make it ambiguous. It didn't make any sense to me why having a secondary failback sensor messes things. But, if you put it in the training data context, it absolutely makes sense.
Just because the human in the scenario only took vision as input, why does that matter to the training data and the model? The actions are the same.
To put it another way, what about all the cultural context the human had, or the sounds, smells, past experiences at the same intersection, etc? Even Tesla can't record this, but I'm not sure that matters.
Tesla wants to make EVs that look like normal cars (Cybertruck being the oddball here, admittedly).
I'm working on a similar problem in computer vision and we're quickly approaching the point where our pure vision work is better than our Lidar supported track because we've had to deal with the constraints instead of having a crutch to lean on.
With computers driving: traffic light turns green. All cars simultaneously start driving. It'd be like a train but without the efficiency.
Similarly, with human drivers: some jackasses drive into the box and the light turns red. Now perpendicular traffic is either fully blocked or must proceeed slower to maneuver around the jackasses. With computer drivers, they shouldn't intentionally break the law and they should have plenty of sensors to figure out that they cannot make it through the box.
Most traffic jams are caused by accidents or people slamming the brakes
Yep, here in Chicago you might even go as many as 12 hours between such events
Imagine a busy intersection where all the cars fly past one another at 40 miles an hour without stopping but none of them crash. Humans can't do this, but machines could, if, and when the technology gets there. To be clear, there's still a way to go.
Also, this already exists in some places. Look at a video of how to cross the street as a pedestrian in Vietnam: You literally just start walking across and people weave around you. Or look at driving in India and similar places.
All I'm saying is never say never
I don't know that you'd ever see this in practice, but it's much more practical in theory for almost identical machines running the same software than for a bunch of humans in a variety of vehicles who've maybe only half understood how to do this.
Also, for this specific problem we know humans are idiots. They should all be driving an agreed route to the agreed evacuation point, but some real humans will decide they know a shortcut, they want to drop past Jim's place, or whatever. Just as there's a difference between what the protocol says happens when you have to abandon an aircraft on the tarmac versus the reality that people will decide they want to self-evacuate and they need their carry on bags and chaos ensues and maybe people die.
But in the future, if there is a coordination standard among driverless cars, that could allow much higher density at higher speed. Coordination standards + higher density of self driving should reduce the self driving cars doing random shit too.