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.
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.
[flagged]
> Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.
https://news.ycombinator.com/newsguidelines.html
Yeah was a joke as I think most cars if you drive through that your car is f'd
Ironically, a properly sealed EV system would better deal with a flood. Combustion engines have issues due mostly to the air intake and exhaust.
You'd need to ensure every electrical connection is in a waterproof location which I'm pretty sure is not a thing for any standard car manufacturing. Cabins are also rarely watertight.
AFAIK your best bet is a diesel with a snorkel, and hope things have dried off before you need to restart the engine.
Rivan's R1T and R1S have a water fording height of ~43", standard: - https://rivian.com/support/article/what-is-the-water-fording...
If cabins are water tight you risk carbon monoxide poisoning
Carbon monoxide... in an EV?
Are EVs typically "sealed" by default? If not, how is going through the effort to "seal" an EV different than installing snorkels for ICE cars?
Some are more sealed than others, such as Rivian's R1T and R1S which have a water fording height of ~43": https://rivian.com/support/article/what-is-the-water-fording...
Their account is about as old as yours.
More a comment on how HN has devolved in the past 2 years, if I want snark, this isn't the place I go to find it.
Really? I find more bad faith and snark on HN than anywhere else and always have.
People are allowed to joke. We don't always need 'substantive comments'
It's not a joke if it doesn't make any sense, what good is a snorkle on an electric car?
That's...the joke. The humor is in the absurdity of recommending an addon to the car that utterly would not work and would look ridiculous. It's layered on the fact that Jeep snorkels look sort of ridiculous even on the vehicles they were designed for.
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.
It shows up on social media when it’s a rare event for that area. It’s uncommon but “happens all the time” here in California in the deserts every heavy rain either because locals forget how deep the flood control washes are, or because tourists just drive into them thinking its a straight road, despite all the signs and warnings posted around them.
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?
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.
> 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.
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.
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 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.
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).
You can have intelligence with lidar.
You can have even more intelligence with both.
Naaah, Tesla has no edge in intelligence either. It's just a PR piece to sell to investors.
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...