To me this doesn't seem like a disaster but just the kind of thing that happens as you role out a service and expose it to new challenges.
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.
I am a little worried that this is still a problem after 20 years. Don't they have simulators to test every weird and unexpected road condition offline? And flooded roads aren't exactly an unusual event to begin with.
They can simulate "driving out of a raging fire" but not a flooded street? This seems like an admission that the fancy "world model simulation" doesn't actually mean much
IMO there is a lot of daylight between “is not perfectly capable of simulating all situations and always used perfectly to the full capabilities of the system” and “doesn’t mean much”.
It may not be usual in Atlanta itself, but living on the Southeastern coast within a mile or two of the water, for 30+ years, it’s a surprisingly common occurrence. I’ve got old photos around of kayaking through downtown Charleston during college, for instance, where the street flooding is not only usual but a many times per season occurrence. Lots of seaside areas have the same issue.
If your premise is "robotaxis are so much better than human drivers" then this is almost a disaster. This is only the 10th city they've deployed to, all in the south, and nowhere there's significantly inclement weather. It does not bode well for their expansion plans.
> This is only the 10th city they've deployed to, all in the south, and nowhere there's significantly inclement weather
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 would assume that after the very first instance you would start moving to fix it. To be in a position where you have to roll back your plans doesn't seem like a simple "delay."
The question is: why haven't you fixed this already?
> The question is: why haven't you fixed this already?
Since you're of the opinion that this is taking too long, what do you think is a reasonable time for a fix, and why? I'm assuming Waymo didn't have a team of flood-detection experts twiddling their thumbs waiting to be prompted into action.
Better is an arbitrary statement. If the primary concern is sexual assault robots are a clearly better and this is a non issue. Use a different benchmark like number of jobs and the robot always loses.
This is really my bear case against AI. I am not against it. I actually think it is really neat! But we have been working on driverless cars for how long and spent how much? And still things like a flooded roadway completely throw them.
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?
This is very much expected while the kinks are worked out. The reason Waymo is rolling out their vehicles in Atlanta in partnership with Uber is precisely for scenarios like this. Standard Uber service provides a backstop for when times when Waymos can't fulfill rides.
The "responsible adults" know that chasing perfection gets you nowhere fast. A part of growing up is learning to put up with "good enough".
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".
I was (I think the search bar will prove this out) a pretty committed skeptic of driverless cars, but I've come around on them in some use cases. I'm not optimistic about them on highways. But they solve some important problems in regional/local transit.
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 bullish on AI as a replacement for Uber from airports well behaved climates I frequent but bearish on how long it'll take to actually make a damn for me needing my car in Ohio until the mid-late 2030s at this rate. It's just so close and so far away at the same time.
I will posit something that guides my own thinking about this; robotaxis will never drink and drive. I'll take whatever flavor of mistake they conjure over that. I can deal with stupidity, I cannot (and don't want to) deal with malice.
"No DUI" is a big part of why even the current, flawed and markedly subhuman, self-driving cars casually beat human drivers on road safety.
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.
Motorcycling used to be one of my biggest hobbies.
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.
Tbf, I think you’re just experiencing a downside of living in NYC. I’ve only ever been there as a tourist, but I wouldn’t ever dream of renting a motorcycle in the city for the reasons you mention.
For context, I live in a highly dense European country and I wouldn’t ride my motorcycle in our most densely populated city centers either. For me, a motorcycle is luxury transportation for when the weather is cooperative or I want to enjoy the journey to my destination. If I want an efficient commute, I’m gonna take the train into the city and enjoy the relaxed state of mind knowing I don’t have to navigate.
Drivers have waaaay too many distractions nowadays and I don’t trust most people to be paying attention as much as I want them to. At least out on the open highway, I stand a chance of getting away from them and putting distance between us. In a city, my options to create space often don’t make much of a difference due to congestion in general.
I hope you can find the opportunity to ride more in the future. :)
Driving through an obviously flooded street thinking "I'll easily make it" and getting stuck in the middle? Yeah, these cars have achieved human level intelligence.
jeep snorkels are for air intakes for engines. electric cars don't have air intakes. they have air cooling for batteries... I suppose you could snorkel those.
A decent welder should be able to turn out a trailer hitch <=> outboard motor bracket in under 15 minutes. It's not like you'll need much more than a modest fishing outboard to get through flooded spots.
That being said... it's actually somewhat uncommon for humans to drive into flooded streets. To the degree that people think it's notable enough to take videos and post them to social media. I don't have the data, but would be interested to see how many times per passenger mile travelled human-directed and remotely-operated vehicles like Weymos drove into flooded streets.
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.
As far as I can tell from these articles, driving into a flood has happened twice to Waymos, once in Texas and once in Atlanta? It does seem like it's pretty uncommon.
Ask the car, in the sense you can, why it drove into the water.
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.
This is why I personally feel like Tesla's approach is more likely to "win". The fundamental blocker to self-driving cars is not sensing / sensor fusion, it is intelligence. And the Tesla approach seems much more likely to achieve functional intelligence than Waymo's.
While I agree with basically all of this, and find the FSD on my Tesla to be quite useful, a question pops into my mind.
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?
> such that they can use the right set of sensors in the right environmental conditions
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.
They could in theory. If they put at least as much emphasis on the AI side as Tesla does. Or if someone else cracked vehicle AI wide open and left it open for them to copy, and then they did exactly that, and found a way to bolt on their extra sensors in a useful fashion while at it.
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.
I thought about this and I think it boils to how the model is trained.
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.
This is an interesting viewpoint, but isn't it also solveable?
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.
Because they don't have a fleet of millions of people labeling the data for them and paying for the privilege of doing so. Waymo has about 3700 vehicles. Tesla has millions. Waymo only operates in known environments and collects a very limited range of data. Tesla collects data everywhere that people drive their cars.
I got downvoted for saying this last time the topic came up but constraints focus a project. It’s best to start work with as few variables as possible, and only add new ones when absolutely necessary.
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.
I agree, but these are also the exact constraints that lead to an early leader getting overtaken by a longer term, yet better set of plans. Not saying that's the case here, but given how much success Waymo has had so far, over really everything Tesla has produced, says quite a bit about the likelihood of the approach, even if it's not yet there.
Snark aside, there will probably always be conditions in which waymo is not the right answer. Are they going to do hurricane evacuation? I think removing the driver just necessitates this.
While this is going to be an overly optimistic scenario: Imagine how smooth a hurricane evacuation would go if _everyone_ used a self-driving car to do the evacuation - atleast there might be less gridlock than there is during any usual hurricane evacuations. And assuming the self driving cars don't do something stupid that causes every car behind it to essentially lock up and stop moving
That said, I know a scenario like that would never happen, probably for the best.
The problem is they're not designed for that. They aren't spending resources on some master control networking system because in 99% of use cases that won't be useful anyways as most of the traffic being dealt with isn't other waymo's willing to communicate.
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.
Sort of. There is no built in support for evacuation methods, but the WayMo absolutely does use a master control system for network the cars. This is how the database of streets is kept and is why WayMo vehicles occasionally swarm private non through way ally streets when there is some glitch in the database that indicates private ways are available roads or an ally that looks like a through way turns out to have a fence between properties.
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.
With human drivers: traffic light turns green. The first car starts driving. The 2nd car waits 2 seconds and then starts driving. The third car waits another 2 seconds (4 seconds total) and then starts driving. The fourth car waits another 2 seconds (6 seconds total) and then starts driving. etc.
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.
Safety margins still will require some level of delay between cars that aren't mechanically linked. Even with perfect reaction times, the physics of driving (maximum acceleration rates, possible loss of traction) dictate this, it's a non-trivial control theory problem. Besides, it doesn't seem to be a goal of Waymo; I've seen lines of their vehicles before and they all behave the same way as in mixed traffic.
As a sorta informed outsider, conceptually this makes intuitive sense. But in practice, how does this work? It seems a lot of the intuition breaks down if we don't assume it's network (aka 1 vendor). Fundamentally it's a bunch of external actors where we cannot verify trust and in order to solve for the needs of the individual, suboptimal choices must be made. To put it another way, even if computers can drive cars, what _else_ needs to be in place for this vision?
Traffic is usually caused by adding inefficiencies across a system with little slack - someone brakes too hard or too early, and if all the cars are stacked up, that one brake event can ripple through hundreds of following cars, getting worse and worse because each person brakes more. Self driving cars can perfectly sync up and move like a train. Theoretically there could be no traffic on highways if all cars are self-driving. Rarely is a highway so full that there couldn’t be more cars (eg. The entrance ramps are backed up) which implies the issues are related to the driving flow and not the capacity of the street itself.
Ideally, robot drivers will some day be better drivers than humans in all road conditions. They'll be able to coordinate fast lane merges and busy intersections by subtly adjusting speed without vehicles having to stop.
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.
Once all cars are autonomous, that day is certainly coming. Even before then, it's very likely we'll see platooning in the future, even if there are still some human drivers.
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.
Self driving cars don't panic and drive recklessly. I don't live in hurricane country, but most accidents around here are caused by drivers who are on their phones/spacing out or driving super aggressively.
Most traffic jams are caused by accidents or people slamming the brakes
In principle the driverless cars are more able to organize fleeting, operating in a way that's not actually practical if you don't share a single guiding directive.
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.
Same reason there's less gridlock when people obey traffic lights and other rules of the road and don't brake randomly. If every car on the road drove itself then there would never be traffic.
Well, probably not the current generation of driverless cars. Those would be a nightmare. Contrary to what some want to believe self driving cars do random shit all the time.
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.
"assuming the self driving cars don't do something stupid"
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.
It would be a failure. Turns out they do something stupid. People tested this in sf by calling a bunch of waymos at once for a prank, but I guess that is the best case example of what a panicked evacuation on the service might be like. It was like a ddos attack. They ended up gridlocking themselves and turned it into a real life version of one of those rush hour board games. No one got out of the little area they called the waymos in.
I doubt it's less actual throughput in most cases. In a place like Atlanta there's no place where it's bus after bus. The BRT line they built nearby is a bus every 10 minutes. Which being very generous to the bus usage is equivalent to like 5 cars a minute.
Evacuation is a use case in my mind. Having a fleet of shuttles on command to move people in preparation of a hurricane would be a benefit. They would obviously need to put weather limitations during actual storms because no one should be driving in a hurricane.
Evacuation you want to prioritized throughput - think of how little road space 100 people in a bus take up vs say 50 cars with 2 people each. Or even 25 cars with 4 people each.
If you have central control you might even be able to get away with changing the rules. i.e. most roads are now one-way leading out of the city. voilà we nearly doubled outbound throughput. Even just for commuting that would be awesome, not that it is happening anytime soon, but one can dream, especially while sitting in gridlock traffic.
Having the middle of five lanes change direction depending on the hour is fairly common. There's even a dedicated machine to move a concrete barrier to support this.
Guessing the depth of a puddle is not an easy task. Many untrained horses will refuse to step into shallow puddles. Then we also have human drivers driving into flooded road.
I wonder how much of this is trouble perceiving water depth vs integrating that understanding into the larger driver model without creating regressions elsewhere.
I don't think there's a good solution right now. You can't just go based on surrounding traffic because humans are also stupid and flood their cars all the time.
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.
> Alternatively, just don't run Waymo operations during flood/flash flood warnings.
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.
Their fleet is constantly scanning the area with lidar, which is assembled into maps. If those maps are in 3d rather than a 2d road grid you can calculate puddles very accurately with no extra sensors:
- 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
The vehicles have enough information to make the determination. Ground data is available in the point cloud and usually labeled as such. Water sometimes shows up in point clouds, sometimes it doesn't depending on conditions and wavelength.
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.
I feel like re-reading this sentence a few times sends me right to the twilight zone of AI psychosis.
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.
Maybe a dumb question, why do electric cars have issues with water?
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 drive through surprisingly deep water, but you'd still rather avoid it for a lot of reasons. Dangerous loss of traction and risk of getting swept away, soaked passengers will want a refund, and a sopping wet interior will take the vehicle out of service for a while.
that and the seal for the battery enclosure can seize up after continuous drives through dirty water, the next passenger may not be so lucky and end up stranded once water breaches the battery pack
Another reason water and ICE cars don't mix is the wiring harness. Even if you don't flood the engine, you'll be having trouble with the electrical for the rest of the car's life. (Or, at least, that's the conventional wisdom)
What are the chances that google just shuts down waymo once they get whatever they need from it. Weren't there other ambitious projects under google that had a similar fate?
During the “winter”, sure, but it dumps rain during the same and there are flash floods occasionally. I agree with the parent comment that Miami is a great area to test - especially given that the bad weather is seasonal. They can run 24/7 during the good weather seasons.
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.
Unpredictable drivers aren’t a challenge compared to weather. They’re just 3D objects to avoid. That’s a solved problem.
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.
This is just part of the slog that autonomous driving was always going to be.
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 another way of framing it is "Waymo pauses Atlanta service due to weather conditions", which doesn't sound at all unreasonable to me. It's no different from "Chicago O'Hare pauses flight departures due to a winter storm" or whatever.
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...
Well except that there were incidents of cars getting stuck in floods with passengers before they paused the service.
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.
Self driving will never handle all corner cases until they essentially have a frontal cortex. They probably need something like an LLM to help with very high level abstract situations, e.g. avoiding a hurricane like someone else mentioned in this thread.
A frontal cortex isn't enough; there are plenty of corner cases that humans fail at too. The real test is if self-driving performs on par, or better than, humans in the vast majority of cases. If it saves 50,000 lives a year to go with self-driving, it's a net-win even if there are a few people who die in situations where they would have survived with a human driver behind the wheel.
Self driving cars are not going to be accepted if they have only marginally better success rates than humans. Just look at the news. Every minor self driving incident is endlessly magnified by the media while millions of human-caused accidents are just a part of life. That's just how our brains work. All major decisions are made primarily based on emotion, not analytics.
Human accidents don't get treated as "just a part of life", serious human driving errors are often considered so egregious that the person making the error picks up a driving ban or even a custodial sentence.
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)
There are over 6 million auto accidents in the US per year. How many of them make the news? I'm willing to bet that most people don't even know about pedestrian deaths that occur a few blocks away from where they live, at intersections they walk through every day. Meanwhile the same people will read about how a self driving car got into a fender bender on the other side of the country and confidently proclaim "this technology isn't safe, I'm never going to use it".
Humans don't handle all corner cases. People can be slow to react to completely novel or surprising situations. There will be corner cases where humans generally do better than a machine, but the simple rule to slow down and come to a halt if things look too weird or confusing will almost always be the right answer.
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.
Not only are people slow to react to unusual situations, but this is taken advantage of by city designers to force people to slow down.
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.
I think self-driving cars will only become better once they can do all the learning in real time and on-board. Otherwise, they will only be as good as the data they trained on - which is ultimately real meat driver data and a derivations of said data.
They will add flooded streets to the training simulation and this problem will go away. Eventually, the corner cases not in the training simulation will be so corner they basically never happen. Waymo can be incredibly successful without dealing with "surprise clown parade" or whatever.
The driving ML model will take care of the next 10 seconds of driving, in a fast loop deciding what steering and throttle commands to give.
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.
they should probably put some sort of metal strip into the roads that a vehicle can follow reliably, future iterations could make continuous contact to the strip to deliver power to these vehicles, and this would also allow them to become larger by reducing fuel weight or even allow cars to travel very close together for efficiency gains
Clearly they haven't actually had any serious problems getting stuck or anything because it'd be all over the news.
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.
> One of Waymo’s robotaxis was spotted driving through a flooded street in Atlanta, Georgia on Wednesday before it ultimately got stuck for about an hour, according to local news reports. The vehicle was recovered and removed from the scene, Waymo told TechCrunch. Waymo says it paused service in the city, just like it has in San Antonio, Texas, while it figures out a solution.
That title sounds so much more dramatic than it seems it actually was. I imagine headlines like: “Billions of python 3.14.4 programs were recalled today when a bug was found in the core itself. No word yet on whether the successor product, Python 3.14.5, will avoid a similar fate. How long will we tolerate being used as test subjects in the developer’s risky games?”
How would you phrase the headline? I think it's pretty accurate, they have pulled thousands of vehicles out of service and completely stopped service in two cities, and the reason is literally that one of their cars was swept into a creek (in addition to other flood-related incidents). I can't think of a way to make the headline any more clear.
This isn't like other software "recalls" where the result is just an over-the-air update or a request to bring your car to a dealership when you have time, in this case they have actually physically removed the recalled vehicles from the road.
To use your analogy: if a bug in Python caused the PSF and package managers to actually make 3.14.4 unavailable and companies started taking Python services offline until a fix was found, yes that would be a really big deal.
I thought Weymo's were supposed to be "supervised" by humans in the Philippines. Maybe driving in circles in the suburbs and driving into flood waters happens only when the cars are out of mobile data range? Did Weymo pay their mobile phone bill? Does the (somewhat) autonomous system on the car decide when to flag a human for help? I would have expected a human to be watching all the time. Are they experiencing labor problems in the Philippines? Maybe Weymo doesn't want to pay their remote operators as much as the remote operators want to get paid?
To me this doesn't seem like a disaster but just the kind of thing that happens as you role out a service and expose it to new challenges.
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.
I am a little worried that this is still a problem after 20 years. Don't they have simulators to test every weird and unexpected road condition offline? And flooded roads aren't exactly an unusual event to begin with.
They can simulate "driving out of a raging fire" but not a flooded street? This seems like an admission that the fancy "world model simulation" doesn't actually mean much
https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-f...
IMO there is a lot of daylight between “is not perfectly capable of simulating all situations and always used perfectly to the full capabilities of the system” and “doesn’t mean much”.
The fact that they aren't a usual event is probably exactly the challenge here.
It may not be usual in Atlanta itself, but living on the Southeastern coast within a mile or two of the water, for 30+ years, it’s a surprisingly common occurrence. I’ve got old photos around of kayaking through downtown Charleston during college, for instance, where the street flooding is not only usual but a many times per season occurrence. Lots of seaside areas have the same issue.
If your premise is "robotaxis are so much better than human drivers" then this is almost a disaster. This is only the 10th city they've deployed to, all in the south, and nowhere there's significantly inclement weather. It does not bode well for their expansion plans.
> This is only the 10th city they've deployed to, all in the south, and nowhere there's significantly inclement weather
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.
It's a delay. The question is how long? Doesn't seem unfixable.
I would assume that after the very first instance you would start moving to fix it. To be in a position where you have to roll back your plans doesn't seem like a simple "delay."
The question is: why haven't you fixed this already?
> The question is: why haven't you fixed this already?
Since you're of the opinion that this is taking too long, what do you think is a reasonable time for a fix, and why? I'm assuming Waymo didn't have a team of flood-detection experts twiddling their thumbs waiting to be prompted into action.
Better is an arbitrary statement. If the primary concern is sexual assault robots are a clearly better and this is a non issue. Use a different benchmark like number of jobs and the robot always loses.
This is really my bear case against AI. I am not against it. I actually think it is really neat! But we have been working on driverless cars for how long and spent how much? And still things like a flooded roadway completely throw them.
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?
This is very much expected while the kinks are worked out. The reason Waymo is rolling out their vehicles in Atlanta in partnership with Uber is precisely for scenarios like this. Standard Uber service provides a backstop for when times when Waymos can't fulfill rides.
The "responsible adults" know that chasing perfection gets you nowhere fast. A part of growing up is learning to put up with "good enough".
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".
I was (I think the search bar will prove this out) a pretty committed skeptic of driverless cars, but I've come around on them in some use cases. I'm not optimistic about them on highways. But they solve some important problems in regional/local transit.
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 bullish on AI as a replacement for Uber from airports well behaved climates I frequent but bearish on how long it'll take to actually make a damn for me needing my car in Ohio until the mid-late 2030s at this rate. It's just so close and so far away at the same time.
I actually took a waymo down North Ave (where one got stuck) a few weeks ago and it was very pleasant.
I'm pretty conservative about this stuff but the waymo is genuinely nice to ride in.
I will posit something that guides my own thinking about this; robotaxis will never drink and drive. I'll take whatever flavor of mistake they conjure over that. I can deal with stupidity, I cannot (and don't want to) deal with malice.
"No DUI" is a big part of why even the current, flawed and markedly subhuman, self-driving cars casually beat human drivers on road safety.
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.
Tesla's self driving will pull over if it detects the human driver has fallen asleep.
Motorcycling used to be one of my biggest hobbies.
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.
Tbf, I think you’re just experiencing a downside of living in NYC. I’ve only ever been there as a tourist, but I wouldn’t ever dream of renting a motorcycle in the city for the reasons you mention.
For context, I live in a highly dense European country and I wouldn’t ride my motorcycle in our most densely populated city centers either. For me, a motorcycle is luxury transportation for when the weather is cooperative or I want to enjoy the journey to my destination. If I want an efficient commute, I’m gonna take the train into the city and enjoy the relaxed state of mind knowing I don’t have to navigate.
Drivers have waaaay too many distractions nowadays and I don’t trust most people to be paying attention as much as I want them to. At least out on the open highway, I stand a chance of getting away from them and putting distance between us. In a city, my options to create space often don’t make much of a difference due to congestion in general.
I hope you can find the opportunity to ride more in the future. :)
I’ve just been to Austin where self-driving cars are everywhere but found to my disappointment that they can’t do trips to the airport.
To your point, knowledge work, _as a whole_ is a much larger and complex domain than self-driving.
The reason they can't do trips to the airport is regulatory and not technical.
Driving through an obviously flooded street thinking "I'll easily make it" and getting stuck in the middle? Yeah, these cars have achieved human level intelligence.
Just get a jeep snorkle
jeep snorkels are for air intakes for engines. electric cars don't have air intakes. they have air cooling for batteries... I suppose you could snorkel those.
Depends on the EV. Some of them have liquid cooling for their battery pack.
What happens when you you start floating?
I guess water propulsion... and a rudder?
You need to get an armored jeep then
A decent welder should be able to turn out a trailer hitch <=> outboard motor bracket in under 15 minutes. It's not like you'll need much more than a modest fishing outboard to get through flooded spots.
That being said... it's actually somewhat uncommon for humans to drive into flooded streets. To the degree that people think it's notable enough to take videos and post them to social media. I don't have the data, but would be interested to see how many times per passenger mile travelled human-directed and remotely-operated vehicles like Weymos drove into flooded streets.
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.
As far as I can tell from these articles, driving into a flood has happened twice to Waymos, once in Texas and once in Atlanta? It does seem like it's pretty uncommon.
Let’s redirect the problem: it’s not the car, it’s the flooding! We should address that first
Ask the car, in the sense you can, why it drove into the water.
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.
This is why I personally feel like Tesla's approach is more likely to "win". The fundamental blocker to self-driving cars is not sensing / sensor fusion, it is intelligence. And the Tesla approach seems much more likely to achieve functional intelligence than Waymo's.
While I agree with basically all of this, and find the FSD on my Tesla to be quite useful, a question pops into my mind.
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?
> such that they can use the right set of sensors in the right environmental conditions
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.
But why can't you apply all that same logic and processing to LIDAR as well. Maybe we're not there yet, but about about in 5-10 years when we are?
There is noise on LIDAR returns too. No one considers a single LIDAR point to be a collision hazard.
They could in theory. If they put at least as much emphasis on the AI side as Tesla does. Or if someone else cracked vehicle AI wide open and left it open for them to copy, and then they did exactly that, and found a way to bolt on their extra sensors in a useful fashion while at it.
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.
I thought about this and I think it boils to how the model is trained.
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.
This is an interesting viewpoint, but isn't it also solveable?
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.
The biggest issue with using both camera and lidar is how to properly resolve conflicting returns from different sensor types.
Because they don't have a fleet of millions of people labeling the data for them and paying for the privilege of doing so. Waymo has about 3700 vehicles. Tesla has millions. Waymo only operates in known environments and collects a very limited range of data. Tesla collects data everywhere that people drive their cars.
The main reason Tesla's don't have LIDAR is hardware cost and maintenance cost, not improved safety.
Maybe also that cars with a LIDAR rig on the roof are appallingly ugly.
Tesla wants to make EVs that look like normal cars (Cybertruck being the oddball here, admittedly).
I got downvoted for saying this last time the topic came up but constraints focus a project. It’s best to start work with as few variables as possible, and only add new ones when absolutely necessary.
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.
I agree, but these are also the exact constraints that lead to an early leader getting overtaken by a longer term, yet better set of plans. Not saying that's the case here, but given how much success Waymo has had so far, over really everything Tesla has produced, says quite a bit about the likelihood of the approach, even if it's not yet there.
You can have intelligence with lidar.
You can have even more intelligence with both.
They never advertised that they did. Its not even real true AI. They just struggle with new scenarios.
People drive into floods too. They just don't get sensational articles written about it, just posted on reddit.
Taxi drivers with passengers don’t tend to though. At least not at the same rate.
Whoosh...
Snark aside, there will probably always be conditions in which waymo is not the right answer. Are they going to do hurricane evacuation? I think removing the driver just necessitates this.
While this is going to be an overly optimistic scenario: Imagine how smooth a hurricane evacuation would go if _everyone_ used a self-driving car to do the evacuation - atleast there might be less gridlock than there is during any usual hurricane evacuations. And assuming the self driving cars don't do something stupid that causes every car behind it to essentially lock up and stop moving
That said, I know a scenario like that would never happen, probably for the best.
The problem is they're not designed for that. They aren't spending resources on some master control networking system because in 99% of use cases that won't be useful anyways as most of the traffic being dealt with isn't other waymo's willing to communicate.
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.
Sort of. There is no built in support for evacuation methods, but the WayMo absolutely does use a master control system for network the cars. This is how the database of streets is kept and is why WayMo vehicles occasionally swarm private non through way ally streets when there is some glitch in the database that indicates private ways are available roads or an ally that looks like a through way turns out to have a fence between properties.
> atleast there might be less gridlock
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.
Why would there be less gridlock if people were in a driverless car instead of a regular car?
With human drivers: traffic light turns green. The first car starts driving. The 2nd car waits 2 seconds and then starts driving. The third car waits another 2 seconds (4 seconds total) and then starts driving. The fourth car waits another 2 seconds (6 seconds total) and then starts driving. etc.
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.
Safety margins still will require some level of delay between cars that aren't mechanically linked. Even with perfect reaction times, the physics of driving (maximum acceleration rates, possible loss of traction) dictate this, it's a non-trivial control theory problem. Besides, it doesn't seem to be a goal of Waymo; I've seen lines of their vehicles before and they all behave the same way as in mixed traffic.
As a sorta informed outsider, conceptually this makes intuitive sense. But in practice, how does this work? It seems a lot of the intuition breaks down if we don't assume it's network (aka 1 vendor). Fundamentally it's a bunch of external actors where we cannot verify trust and in order to solve for the needs of the individual, suboptimal choices must be made. To put it another way, even if computers can drive cars, what _else_ needs to be in place for this vision?
Traffic is usually caused by adding inefficiencies across a system with little slack - someone brakes too hard or too early, and if all the cars are stacked up, that one brake event can ripple through hundreds of following cars, getting worse and worse because each person brakes more. Self driving cars can perfectly sync up and move like a train. Theoretically there could be no traffic on highways if all cars are self-driving. Rarely is a highway so full that there couldn’t be more cars (eg. The entrance ramps are backed up) which implies the issues are related to the driving flow and not the capacity of the street itself.
> Rarely is a highway so full that there couldn’t be more cars
Yep, here in Chicago you might even go as many as 12 hours between such events
Ideally, robot drivers will some day be better drivers than humans in all road conditions. They'll be able to coordinate fast lane merges and busy intersections by subtly adjusting speed without vehicles having to stop.
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.
Evidence suggests... no, that day is never coming.
Once all cars are autonomous, that day is certainly coming. Even before then, it's very likely we'll see platooning in the future, even if there are still some human drivers.
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
Right… any time now.
If you want to write with such confidence perhaps you should share what the lottery numbers are?
Never is a long time though. Even if it takes 500 years for that to happen, it will still have happened.
busy intersections have more than just cars, my jay walking is going to cause a massive pile up
Self driving cars don't panic and drive recklessly. I don't live in hurricane country, but most accidents around here are caused by drivers who are on their phones/spacing out or driving super aggressively.
Most traffic jams are caused by accidents or people slamming the brakes
In principle the driverless cars are more able to organize fleeting, operating in a way that's not actually practical if you don't share a single guiding directive.
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.
Same reason there's less gridlock when people obey traffic lights and other rules of the road and don't brake randomly. If every car on the road drove itself then there would never be traffic.
This is literally not true, roads still have finite capacity, and sometimes demand exceeds capacity.
Well, probably not the current generation of driverless cars. Those would be a nightmare. Contrary to what some want to believe self driving cars do random shit all the time.
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.
Now imagine if the power is out and cell service is down. We saw that happen in San Francisco and it was chaos.
"assuming the self driving cars don't do something stupid"
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.
It would be a failure. Turns out they do something stupid. People tested this in sf by calling a bunch of waymos at once for a prank, but I guess that is the best case example of what a panicked evacuation on the service might be like. It was like a ddos attack. They ended up gridlocking themselves and turned it into a real life version of one of those rush hour board games. No one got out of the little area they called the waymos in.
I mean the logical conclusion is a dedicated lane for automated cars..
At which point we've reinvented privatized buses with a last mile convenience vs greatly reduced throughput trade-off.
I doubt it's less actual throughput in most cases. In a place like Atlanta there's no place where it's bus after bus. The BRT line they built nearby is a bus every 10 minutes. Which being very generous to the bus usage is equivalent to like 5 cars a minute.
Just take away the sidewalk and bike lane :-/
Evacuation is a use case in my mind. Having a fleet of shuttles on command to move people in preparation of a hurricane would be a benefit. They would obviously need to put weather limitations during actual storms because no one should be driving in a hurricane.
Evacuation you want to prioritized throughput - think of how little road space 100 people in a bus take up vs say 50 cars with 2 people each. Or even 25 cars with 4 people each.
If you have central control you might even be able to get away with changing the rules. i.e. most roads are now one-way leading out of the city. voilà we nearly doubled outbound throughput. Even just for commuting that would be awesome, not that it is happening anytime soon, but one can dream, especially while sitting in gridlock traffic.
Having the middle of five lanes change direction depending on the hour is fairly common. There's even a dedicated machine to move a concrete barrier to support this.
https://www.youtube.com/watch?v=8IwBJPqyoB8
> No one should be driving in a hurricane.
I agree, but there are a number of people here in Florida who will do it or die trying (emphasis on the die trying)
Except the Waymo can do 150 mph bumper to bumper with other Waymos if you let them.
.. well until it hits the flood
Guessing the depth of a puddle is not an easy task. Many untrained horses will refuse to step into shallow puddles. Then we also have human drivers driving into flooded road.
I wonder how much of this is trouble perceiving water depth vs integrating that understanding into the larger driver model without creating regressions elsewhere.
I don't think there's a good solution right now. You can't just go based on surrounding traffic because humans are also stupid and flood their cars all the time.
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.
> Alternatively, just don't run Waymo operations during flood/flash flood warnings.
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.
Their fleet is constantly scanning the area with lidar, which is assembled into maps. If those maps are in 3d rather than a 2d road grid you can calculate puddles very accurately with no extra sensors:
- 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
The vehicles have enough information to make the determination. Ground data is available in the point cloud and usually labeled as such. Water sometimes shows up in point clouds, sometimes it doesn't depending on conditions and wavelength.
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.
Do you how often you get flash warnings in Atlanta? And local roads flood far more often than flash food warnings are issued.
If you can’t handle this issue, you really can’t operate in Atlanta.
Would be interesting if you can compare the surface roughness of pavement vs. the surface of water, wind would disturb it too
In many situations, the depth of the water doesn't matter as driving into it will likely result in death.
I feel like re-reading this sentence a few times sends me right to the twilight zone of AI psychosis.
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.
Maybe a dumb question, why do electric cars have issues with water?
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 drive through surprisingly deep water, but you'd still rather avoid it for a lot of reasons. Dangerous loss of traction and risk of getting swept away, soaked passengers will want a refund, and a sopping wet interior will take the vehicle out of service for a while.
that and the seal for the battery enclosure can seize up after continuous drives through dirty water, the next passenger may not be so lucky and end up stranded once water breaches the battery pack
Another reason water and ICE cars don't mix is the wiring harness. Even if you don't flood the engine, you'll be having trouble with the electrical for the rest of the car's life. (Or, at least, that's the conventional wisdom)
Deep water can still damage an EV by getting into connectors, sensors, wheel bearings, brakes, and cabin electronics.
They can also float just like a regular car.
Yep if they are watertight they will float, if they aren't, they'll fill up with water.
What are the chances that google just shuts down waymo once they get whatever they need from it. Weren't there other ambitious projects under google that had a similar fate?
I assumed they went to Miami to develop their foul weather capabilities. It's still pretty early.
Doubtful. They probably just pause service when it rains. Miami weather is ideal most of the time.
These self-driving companies have made very little progress on dealing with weather for how long they’ve spent on the problem.
During the “winter”, sure, but it dumps rain during the same and there are flash floods occasionally. I agree with the parent comment that Miami is a great area to test - especially given that the bad weather is seasonal. They can run 24/7 during the good weather seasons.
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.
Unpredictable drivers aren’t a challenge compared to weather. They’re just 3D objects to avoid. That’s a solved problem.
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.
Is it so hard for LiDAR/Camera to detect flood water on road. Water on a road looks like a flat surface to sensors.
This is just part of the slog that autonomous driving was always going to be.
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!
It can't mean that, there's a lake there!
I think another way of framing it is "Waymo pauses Atlanta service due to weather conditions", which doesn't sound at all unreasonable to me. It's no different from "Chicago O'Hare pauses flight departures due to a winter storm" or whatever.
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...
Well except that there were incidents of cars getting stuck in floods with passengers before they paused the service.
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.
hard part is that cars should drive through shallow water... but how to know the depth?
given accurate mapping + realtime imaging, this should be possible albeit a Big Project(tm).
Biblical.
Coming to New Orleans soon...
Self driving will never handle all corner cases until they essentially have a frontal cortex. They probably need something like an LLM to help with very high level abstract situations, e.g. avoiding a hurricane like someone else mentioned in this thread.
A frontal cortex isn't enough; there are plenty of corner cases that humans fail at too. The real test is if self-driving performs on par, or better than, humans in the vast majority of cases. If it saves 50,000 lives a year to go with self-driving, it's a net-win even if there are a few people who die in situations where they would have survived with a human driver behind the wheel.
Self driving cars are not going to be accepted if they have only marginally better success rates than humans. Just look at the news. Every minor self driving incident is endlessly magnified by the media while millions of human-caused accidents are just a part of life. That's just how our brains work. All major decisions are made primarily based on emotion, not analytics.
Human accidents don't get treated as "just a part of life", serious human driving errors are often considered so egregious that the person making the error picks up a driving ban or even a custodial sentence.
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)
There are over 6 million auto accidents in the US per year. How many of them make the news? I'm willing to bet that most people don't even know about pedestrian deaths that occur a few blocks away from where they live, at intersections they walk through every day. Meanwhile the same people will read about how a self driving car got into a fender bender on the other side of the country and confidently proclaim "this technology isn't safe, I'm never going to use it".
Getting banned from driving is extremely rare. Most people convicted of DUI are still allowed to drive.
Maybe. But insurance rates, and the government's enforcement of laws, are based on analytics, and overcome a lot of human emotional bias.
Humans don't handle all corner cases. People can be slow to react to completely novel or surprising situations. There will be corner cases where humans generally do better than a machine, but the simple rule to slow down and come to a halt if things look too weird or confusing will almost always be the right answer.
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.
Not only are people slow to react to unusual situations, but this is taken advantage of by city designers to force people to slow down.
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.
I think self-driving cars will only become better once they can do all the learning in real time and on-board. Otherwise, they will only be as good as the data they trained on - which is ultimately real meat driver data and a derivations of said data.
They will add flooded streets to the training simulation and this problem will go away. Eventually, the corner cases not in the training simulation will be so corner they basically never happen. Waymo can be incredibly successful without dealing with "surprise clown parade" or whatever.
this is absolutely already a thing under development, you can see Waymo is hiring for reasoning roles
how would a llm help
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 driving ML model will take care of the next 10 seconds of driving, in a fast loop deciding what steering and throttle commands to give.
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.
Lmao what…
You sound like an econ prof: full of it and hand waving away with hypotheticals.
they should probably put some sort of metal strip into the roads that a vehicle can follow reliably, future iterations could make continuous contact to the strip to deliver power to these vehicles, and this would also allow them to become larger by reducing fuel weight or even allow cars to travel very close together for efficiency gains
you are describing a train
Clearly they haven't actually had any serious problems getting stuck or anything because it'd be all over the news.
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.
> One of Waymo’s robotaxis was spotted driving through a flooded street in Atlanta, Georgia on Wednesday before it ultimately got stuck for about an hour, according to local news reports. The vehicle was recovered and removed from the scene, Waymo told TechCrunch. Waymo says it paused service in the city, just like it has in San Antonio, Texas, while it figures out a solution.
Thousands of Waymos recalled after robotaxi swept into a creek https://www.bbc.co.uk/news/articles/cwy2011dl4xo
> 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.
That title sounds so much more dramatic than it seems it actually was. I imagine headlines like: “Billions of python 3.14.4 programs were recalled today when a bug was found in the core itself. No word yet on whether the successor product, Python 3.14.5, will avoid a similar fate. How long will we tolerate being used as test subjects in the developer’s risky games?”
How would you phrase the headline? I think it's pretty accurate, they have pulled thousands of vehicles out of service and completely stopped service in two cities, and the reason is literally that one of their cars was swept into a creek (in addition to other flood-related incidents). I can't think of a way to make the headline any more clear.
This isn't like other software "recalls" where the result is just an over-the-air update or a request to bring your car to a dealership when you have time, in this case they have actually physically removed the recalled vehicles from the road.
To use your analogy: if a bug in Python caused the PSF and package managers to actually make 3.14.4 unavailable and companies started taking Python services offline until a fix was found, yes that would be a really big deal.
There was one in Atlanta that made the local news where it went too deep and stalled out, was stuck for over an hour.
does Waymo use Lidar or is it like Musk's "cost saving" cameras only
Waymo uses lidar. There's lots of information about it on the web.
The spinny things on the vehicle are LIDAR.
Lidar is much less accurate in the rain.
If they only would use lidar. Oh wait…
I thought Weymo's were supposed to be "supervised" by humans in the Philippines. Maybe driving in circles in the suburbs and driving into flood waters happens only when the cars are out of mobile data range? Did Weymo pay their mobile phone bill? Does the (somewhat) autonomous system on the car decide when to flag a human for help? I would have expected a human to be watching all the time. Are they experiencing labor problems in the Philippines? Maybe Weymo doesn't want to pay their remote operators as much as the remote operators want to get paid?
Your assumption that Waymos are "(somewhat) autonomous" is wrong, which is why your questions and conclusion don't make any sense
It's an interesting illustration of how widely and quickly misinformation spreads, though.
Maybe the solution is to put in more billions. Every fad creates jobs.