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.
I can appreciate the cameras and lidar on the Weymos don't give their remote operators a lot of good data about the depth of water on the road-way. As you point out, humans in cars often don't get this right. I think the humans that don't drive into deep water are the ones who a) give any amount of water on the roadway a big NOPE and b) people familiar with the local environment and use multiple visual clues to judge the true depth of the flooding.
Then ask the human.
I'm not sure you'd walk away the idea that they have equivalent intelligence. The human at least knew the water was there and took a risk, the car, presumably, had no idea what was in front of it and drove into it anyways.
I guess water propulsion... and a rudder?
Toyota uses air cooling (from the cabin) for their hybrid batteries, as do others.
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?
Tesla trains it models from actual drivers purely based on (input) Vision and (output) actuators - Brake, Steering, Accelerators.
Human output is based on what they and the camera sees. So, it's a 1:1 match.
If Waymo were to do that, it'll muddle the training set. The Lidar input may override camera input.
I always struggled when Musk mentioned Lidar will make it ambiguous. It didn't make any sense to me why having a secondary failback sensor messes things. But, if you put it in the training data context, it absolutely makes sense.
Just because the human in the scenario only took vision as input, why does that matter to the training data and the model? The actions are the same.
To put it another way, what about all the cultural context the human had, or the sounds, smells, past experiences at the same intersection, etc? Even Tesla can't record this, but I'm not sure that matters.
I'm exaggerating, but I hope you get the point. It isn't even conflicting sensor signals about the pothole, but conflicting information about the causation. With vision only there is no conflict for the training data. This was my Aha moment. Multiple Sensors are absolutely important for fallback and extra safety, but screws up training that are based on Human Drivers
I think Elon himself doesn't understand this and hence can't articulate it, while just repeating whatever his ML engineer has said.
Because this part is really hard, and that's why Tesla abandoned the fusion approach. You cannot possibly foresee all the conditions in which LIDAR or any active sensor will malfunction/return wrong data/return data that's only slightly off for that ONE specific time. And even if it doesn't, you need to trust it to not return noise. And when it does return noise, how do you classify it as noise?
Cameras are passive sensors - they get whatever light comes in and turn it into an image. Camera is capturing shapes that make sense to the neural nets: it's working. See all black/white/red/cannot see any shapes? Camera is not working, exclude it from the currently used set of sensors or weigh it less when applying decisions, because it's returning no signal (and yes, neural nets have their own set of problems).
EDIT: cameras also provide more continuous context: if 1 pixel is off, is clearly bright red in a mostly-green scene where no poles can be identified, the neural net will average it out and discard it as noise. If 1 pixel says "object" in LIDAR, do you trust it to be correct? Perhaps the ray just hit a bird or a fly, but you only see a point, it's a lossy summary of the information you need.
As is, Waymo's playing it smarter than Cruise did, but they're not all in on AI yet. So I don't expect them to "leapfrog Tesla" in that dimension - and it's the key dimension to self-driving.
Tesla wants to make EVs that look like normal cars (Cybertruck being the oddball here, admittedly).
I'm working on a similar problem in computer vision and we're quickly approaching the point where our pure vision work is better than our Lidar supported track because we've had to deal with the constraints instead of having a crutch to lean on.
You can have even more intelligence with both.
Sensor fusion isn't free. Lidar requires more power consumption and more onboard compute. Cycles that could be spent on "intelligence" are instead being spent on sensing.
Tesla's approach seems like a bet that A) AI will reach human-level driving intelligence before lidar becomes cost-efficient, in which case their current sensors will be sufficient to achieve at least human-level performance; and B) ~human-level performance will be sufficient to achieve large-scale consumer and regulatory acceptance. Waymo seems to be taking the other side of that bet.
If Tesla is right, their solution should scale faster, and they can worry about adding superhuman sensory capabilities later. If Waymo is right, all the Cybercabs that Tesla is pumping out right now are destined for the scrapyard, or at best will spin their wheels in beta testing for years while Waymo speeds ahead.
Tesla is putting its money on the bull case for self-driving as a whole. If Tesla wins that bet, it means we all get access to a useful version of the tech years earlier. If Waymo wins, that's great too, but it means that for better or worse lidar will be a bottleneck to scaling the tech.
The whole thing is basically a rehash of Intel vs TSMC on EUV in the 2010s.
People drive into floods too. They just don't get sensational articles written about it, just posted on reddit.