Both from the technical and the marketing point of view having a car that can do better than any human ever could, even in the most optimal of conditions would be a great thing.
I used to drive through torrential storms in Brazil, where the highest level of the wipers was barely enough to keep water out of the windshield, never had an issue when keeping safe speeds either (even though Brazilian traffic is murderous).
Haven't seen an equivalent snowstorm so far. Even in the worst case there's still at least 30 ft / 10 m visible range. Which is not great, but at least something.
In both cases, if at all possible, better to stay put!
You control the hardware and software, you drive in real world conditions, you can deploy regionally and control the hours and conditions you operate in letting you gradually increase when you operate as you judge it safe.
And if the robotaxi makes a mistake, well it's still in testing phase/beta.
If Tesla comes out with FSD then it has to be damned near "perfect" in all conditions, which just is never going to happen out the gate?
Oh! Oh! And then for efficiency maybe you can chain a ton of robotaxis together, so you only need one drivetrain... wait a second...
Anyway, Tesla have supposedly recently bought a massive pile of Luminar sensors, so I expect them to be in one of these vehicles soon.
The problem isn't that the sensors need to be added now, the problem is that without the sensors the cars are half blind and are more likely to make bad decisions and refusing to add them at all shows bad judgement.
Rain, snow, mist, fog, darkness -- these are things cameras are very bad at seeing through. Is that thing in front of us solid? Without extensive training on that particular type of object, the AI with the camera has to guess. I'm sure these aren't the best examples -- I am not an expert in LiDAR. But I do have experience with computer vision with camera systems, and they are woefully insufficient for life and death decision making systems when they haven't been trained on the exact specific scenarios they will encounter.
LIDAR probably would help to make Phantom Brakes happen less often, simply because there would be another info source for "is there REALLY an object that is dangerous to me?".
Those who say that FSD is "pretty good" are living in a fantasy world. There is hard data on miles between critical disengagements (which really should be called "if the driver doesn't respond within a fraction of a second, people will die"), and depending on region, model, weather etc it's between 13 and 115 miles right now.
Over here in Germany there are statistics that a human driver will have the equivalent every 155,000 miles.
"Pretty good" just doesn't cut it when it's about the risk of killing people.
My Model 3 right now detects about 60-70% of school children crossing the road (keep in mind roads in Germany are narrow, and humans including kids are using the roads, too). 30%-40% of those I would kill every morning on my way to the office.
And the thing is: 70% isn't enough for this, 80% isn't, 90% isn't, 99% isn't, and 99,999% isn't.
Side note: People constantly claim that Waymo is autonomous. It's as autonomous as a tram. They only work because it the cities they operate every single road they use have been mapped by hand and is constantly updated. Send a Waymo into my city over here, and will also kill a couple of kids per day. Years ahead of Tesla? Yes. Good enough? Hell no.
It probably is, actually (and sadly).
Looking around at the percentage of drivers around me on the road with their face looking at a phone, while moving, is probably in excess of 5%. These people won't see the kids crossing the road, either.
Given that the leading causes for road accidents are speeding, DUI and distractions [1], a car doesn't need to be better than any human reasonably can. It just needs to be on the same level as a well-rested, sober human without a phone in their hand.
The decision LIDAR vs cameras is the pareto principle in action: what matters is the 80% - the mass that causes the most accidents.
[1] https://www.who.int/news-room/fact-sheets/detail/road-traffi...
The prosperity of technology basically solves the problem with the most terrible hardware possible.
For example, if you have a really good microprocessor that works, what you should do is shrink it until it barely works or speed it up until it barely works to get a similar version for cheaper or faster version for the same price.
Same goes with cars. If you have a car that works with ultrasonic and lidar and cameras, eliminate the expensive sensors and reduce the cost.
Reducing the cost will let more people afford the cars, and sell more cars.
To Musk that is basically his plan all along - to replace ICE cars with EVs. Just wish we could keep turn signal/wiper/headlight stalks.
Well... that conditional clause was never satisfied in the first place ;)
But the point Tesla didn't go through "FSD with ultrasonic + lidar + cameras", and then start to reduce the sensors. They went from "no FSD" to "broken non-working FSD, but now only with cameras". That is not how you strip down a working product to reduce costs.
They shrink their chips, they up their clock speeds and keep the costs the same.
They also remove hardware - they've relentlessly removed ports, intermediate interfaces (like removable batteries, memory and processor sockets) and changed screws into glue.
If Musk had gone all-in on every sensor possible and put them all over the cars like strings of Christmas lights, I assume you would be defending that decision with just as much fervor.
and applying this analogy to the current situation: do we?
normally you have to sell a product that works first, take the profits and then invest that into improving the components to make more profits. I think Elon has cracked the code: sell the product you don't have to people who would love to have it, and you don't even need to produce a version 1 that works - just move on to solving the problems that you want to. the market will pay you either way.
* much faster reaction times
* ability to model multiple possible trajectories
* ability to see in all directions simultaneously
* no distractions, no tiredness, no falling asleep, no health problems
That said, this particular big bet looks pretty bad for Musk. He's been confidently promising self-driving either "this year" or "next year" for the last several years, and so far Tesla appears nowhere close to getting L4/L5 driving down.
And yes, I know the EX90 is currently an absolute mess in terms of software and their LIDAR isn't even enabled for driving assists yet, but the hardware is there so it can be built on scale.
Specifically, loads of object detection with smaller objects, imagine an android cleaning off a table after dinner. Visual spectrum, camera identification is vital here.
So it's all one ball of wax. Tesla is not juet a car company, likely in 5 years the number of androids they sell will dwarf car sales.
But those cameras don't have to be simple visual spectrum cameras, do they? They could have, for example, in each eye, LIDAR, infrared, several focal lengths of visual, etc. Not to mention being able to augment those sensors at places humans just don't get sensor data (e.g: back of head; outward facing from shoulders, hips, etc.; constant presence monitoring via ultrasound / IR / 2.4GHz, etc.)
They do, if they want to do comparative analysis on the trillions upon trillions of existing photographs, and even motion picture frames.
Not to mention the entire AI industry is working full out for perfect image recognition, again in our visual spectrum. Something apparent to a person working with openai 5 years ago.
I have a robomop which does some pretty accurate scanning with its radar. I thought this is cheap, consumer appliance grade technology already.
Why would you ever want an unstable bipedal robot? Alternatives seem way more efficient.
Also are you predicting that Teslas sales will collapse and they’ll lose most of their EV market share? Because that’s a pretty bold take
It also simplifies the stack a lot to have a single set of sensors, so the software becomes mostly: getting good training data (iterative loops from failing production cases) and an efficient training algorithm.
This scales to more than just AD and also can leverage new breakthroughs from academia
I don't doubt that it's possible with machine vision alone, but it makes the challenge substantially harder.
No, humans do significant sensor fusion.
There's binaural audio: useful to detect and have a rough position of emergency services and/or high speeds cars(it's true that its a relatively unencumbered channel, but that makes it all the more valuables for emergencies)
And there's a working if imperfect IMU (performance can be altered if the power supply is set to an alternate mode) to sense all kinds of acceleration: for fine course correction on acceleration and bearing, for getting the road condition and adjust the driving profile accordingly, etc..
It also does away with one of the crucial aspects of "better than human safety" - driving in conditions where a camera is uselss - think moderate to dense fog, heavy rain etc. etc.
Perhaps. But isn’t Waymo way ahead of Tesla?
So it is Lidar + manual work for each area.
While Tesla is trying to go for a solution you can just drop in anywhere.
It makes no sense to say "humans can do it with only X, so machines should have to as well". Do cars run? Do planes flap?
I do agree that planning should be the hardest part of self-driving, but go and look at any Tesla FSD video on YouTube and it's clear they haven't solved the perception part yet either. Cars wobbling all over the place, morphing into vans, etc.
Also, should your eye cameras be in fixed position, you even more urgently should see a doctor. Mine can be turned 180° on the X-axis, and 120° on the Y-axis.
And finally: My eye cameras have working high-speed auto-focus, HDR, are protected against rain or snow, can operate at very low ISO, and have retina resolution.
:)