Sure LIDAR is more expensive but the data quality is very stable and good. Cameras won't work that well or at all in night or when the sun rays blend in morning / evening or in bad weather conditions like heavy rain or snow. All other self-driving cars I know of have at least a small LIDAR beside several other types of sensors.
The rotating Velodyne thing Google uses is an interim research tool. In production, expect to see better design.
With enough LIDAR units, full-circle coverage can be achieved. This is already being done with cameras. The rear and side units need less power and range than the front-facing units, so they can have a smaller collecting aperture, making them easier to place. For the front-facing units, the emitter can be spread out, allowing higher power. The rules on eye safety are concerned with the maximum power coming through a 1/4" hole (human eye pupil). If the laser emitters are spread out over the width of the windshield, the maximum power per unit area is reduced.
The CMU/Cadillac demo a few years ago managed to conceal all the sensors. It can be done. It just costs too much until one of the three companies claiming to be building low-cost automotive LIDARs Real Soon Now manages to deliver.
> ...expect to see better design.
> ...costs too much until one of the three companies claiming to be building low-cost automotive LIDARs Real Soon Now manages to deliver.
All I see is future promises. Tesla wants to do this now, that too at mass market costs, independent of whether those promises materialize or not.
It also depends on how much you trust their R&D team. If they thought this was a week sensor suite and something else was just around the corner, they won't risk fitting their cars with inferior technology which would be obsolete in a couple of years, making all the collected data worthless. Yes I am aware they did this to existing cars lacking the full autonomy hardware, but I find that OK, since, for them, they never promised full autonomy to begin with.
Low-cost LIDAR is a problem that can be solved with money and a customer ready to buy a lot of units. So far, no car company has said "we'll take a million units a year at $100 if they meet these specs." It's lack of demand, not lack of technology. It's a risk for the LIDAR maker. There are automotive radars available better than those currently on cars, but they don't have a volume buyer yet.
Statement A: Tesla is just hyping autonomous driving since no technology can deliver fully autonomous driving right now.
Statement ~A: Low-cost LIDAR is available to any high-volume customer, of which Tesla is one, given Model 3 demand. And LIDAR can deliver fully autonomous driving.
The point simply being, I have a hard time believing that even with good-looking, cheap LIDAR being a possibility, as you claim, Tesla chose to go with an inferior sensor suite, for no apparent reason.
Volvo already has a concealed LIDAR in their self driving car.[1] Volvo will put 100 self-driving cars in the hands of ordinary drivers in Sweden in 2017. This is self-driving by the pros. Makes Tesla look amateurish.[2]
And Volvo is way ahead on marketing self-driving.[3]
[1] https://www.youtube.com/watch?v=2q00jIBhkq4 [2] http://www.volvocars.com/intl/about/our-innovation-brands/in... [3] https://www.youtube.com/watch?v=uDB6fFflTVA
R&D doesn't make top-level budget decisions.
If the product fails, that's on Musk and Musk alone.
He takes credit for its successes. He can take responsibility for its failures too, as we would expect from any respectable CEO.
http://www.sae.org/dlymagazineimages/web/516/11702_15456.jpg
>> then the car doesn't look cool anymore.
>> There are videos of three separate Tesla crashes where the Tesla plowed into a vehicle partially blocking a lane.Spinny LIDAR on top is never gonna look cool unless your aesthetic is "Ghostbusters", but it's also already looking technologically obsolete.
This is actually common problem not only for Tesla. We won't see LIDAR in production cars (you will certainly see them in prototype cars as usual) until its cost drastically reduced
This is something we'll probably see in second-generation self-driving cars. With range-gated LIDAR and millimeter radar, self-driving cars will do far better in fog and heavy rain than humans do.
[1] http://www.sensorsinc.com/applications/military/laser-range-... [2] http://www.obzerv.com/en/range-gating-technology/core-expert...
All you have is two overlapping cameras with poor resolution and they let you behind the wheel, right?
This is a software and processing hardware problem only. Human beings drive OK for the most part and we don't have lidar/radar/sonar/etc.
With 8 cameras providing 360 vision and an advanced NVIDIA hardware suite, this problem is entirely solvable and this is a very reasonable solution to it.
I actually don't know a single expert who thinks we're anywhere close to having a stage 4 autonomous vehicle. Most of the people I respect are pegging it at decades instead of years.
At higher speeds, prediction becomes more of an issue. Will that car on the side street drive into the intersection, or not? At slow speeds, you can wait and see what they do. At higher speeds, you have to predict behavior. That's hard, but a machine learning problem.
I'm not arguing against progress. I think it's great people are working on this and obviously you have to start somewhere. I'm just pointing out the fully autonomous vehicle is not a few years away.
AFAIK deep-learning hasn't really brought much change to this manner of doing things - the mapping part esp.
If you're only reading article after article hyping the technology instead of talking to the people actually building it, of course you're going to believe the journalists instead of the engineers.
I don't know anybody actually writing the code that thinks we're close. Of course the PR teams and managers are going to hype everything up - that's their job.
The guys you spoke to, was this before or after they went to Google/Uber? As I understand the approach at Tesla would be (a) collecting an enormous amount of real-world driving data that possibly others are not or have not done yet, (b) do as little "coding" as possible, but rather take a deep-learning approach. I.e. the "algorithm" gets better the more data you throw at it, it doesn't depend on human intelligence, but rather on how much data you collect. Similar to how Google Translate got so good (it's not good because of any linguistic model or because a team of linguists "coded" it, it's just good because of the sheer amount of data it was trained on). (c) they have enough money to throw at any hardware requirements for a platform that could train on such an amount of data.
Are the conditions above not different perhaps from what you experienced whilst working at NREC and perhaps different from the way you guys approached autonomous-driving? I'm trying to think from Elon Musk first-principles. If it was technically possible, what are all the lego blocks you need to go about building this?
My skepticism is in the leap towards a fully autonomous vehicle, safe for driving on real roads in harsh conditions. Very few people have attempted anything in bad weather yet, although Ford has actually done some work on it.
If we are talking about incremental improvements - sure that will happen constantly. What I'm saying is I don't believe you're going to get into a car without a driver anytime soon.
Tesla is planning to have their factory produce 500,000 cars in 2018. Even if they only meet half of that they'll still be collecting a lot of data.
I would guess most major cities and freeways will have full autonomous support by 2019.
Once you reach a certain critical percentage of fully autonomous vehicles regulation will probably change to such an extent that car insurers will void your insurance if you drive it manually, or give a significant discount to your insurance premium if you don't ever put it in "manual" mode, etc.
I think the problem is really difficult right now because humans are still allowed to drive, but as that ratio goes down it becomes less of a serious problem.
https://www.tesla.com/nl_NL/videos/full-self-driving-hardwar...
And the best that all that processing power can come up with is: tailgating someone. No thanks.
It might rule out driving in fog or snow, but if the weather is OK then the sensor suite is probably enough for driving on any paved road.
I think you are underestimating how quickly Tesla's AI software can improve. The AI will always be running and checking to see if the driver does anything unexpected. For example if you brake or swerve suddenly when the AI did not expect you to do so, all the sensor data and state of the AI will be sent back to Tesla headquarters. Tesla will amass millions of these 'significant events' and be able to back-test updates to their AI logic against every single event. It will be test-driven development on steroids, allowing rapid software improvement while maintaining high quality.
Do you have any basis for that statement, other than that you like Elon Musk, and read an article about "big data" once?
I have actual experience in this field. The parent is right: the sensors matter more than hypothetical gains from "big data". Because right now, sensors suck. There's a reason every serious player in this game is plopping pairs of $10,000 lidar units on their test cars (and still failing to work in the rain). You can't distinguish signal from noise by collecting more noise, and until you have a decent sensor suite, "big data" is just garbage-in, garbage-out.
Reading this discussion, you'd almost forget that the reason Tesla is doing this is because its existing sensor suite was proven to be a total joke in actual use...but hey, here's to the emperor's new outfit! One hundred percent less naked than the old one!
What irq11 has said is valid and important. You cannot just handwave actual engineering problems with some buzzwords
It's valid and important to say, "I have experience in this field and sensor quality will always be more important than a suite of regression tests, no matter how large".
It's not valid and important to say, "You're just saying that because you like Elon Musk, and read an article about 'big data' once"
It would be a perfectly reasonable response to my comment to say "why yes, I do, and here's my evidence (along with a great reason that I didn't provide it in my original comment!)"
It stands to reason that if the parent had a better argument than "test-driven development on steroids", then they'd have provided it. But asserting that someone's well-informed comment about sensor quality "underestimates" the power of big-data-AI-TDD-learning-rapid-innovation-foo, well, that sounds like magical thinking.
In any case, this is a meta-discussion about voting, which is definitely not okay, so I'll leave it at that.
Here's Mobileeye's CTO talking in detail about their technology.[1] They use a a deep neural network that recognizes cars. Just cars. Look at this point in the video.[2] There's a stationary obstacle on the left, and it doesn't recognize that. Maybe this is why Tesla, using Mobileye hardware, plowed into three different stationary vehicles at the left of a highway. They were all trucks (one was a street sweeper), and seen from the back while stationary, may not have matched the trained model of "car".
Mobileeye's algorithms are good at recognizing moving cars from most angles, and that's valuable for feeding into the model of "what's the other player doing". But it's not enough for the "where are all the obstacles" and "where is the road surface" questions. Those questions have objective answers, which can be answered with a LIDAR, and maybe millimeter radar if it has the angular resolution.
[1] https://www.youtube.com/watch?v=GCMXXXmxG-I [2] https://youtu.be/GCMXXXmxG-I?t=1758
The existing sensor suite was is sufficient for the existing driving assist functions. The existing suite was never expected to be sufficient to provide the predicted, future levels of autodrive.
What do human sensors cover? Primarily our eyes. Have we had sensors that provide about the same dynamic range as our human vision?
I am NOT a part of this field, so please excuse my presumption.
I believe that most/all(?) current/previous 'automated control' systems used sensors that were lower resolution than our eyes, but that also provided some indication of range data.
Do our eyes provide distance data? I think that data is calculated with quite a bit of accuracy in our brains.
Again, I'm not part of this field, so this is just speculation.
In theory, given a fast enough computer and advanced enough algorithm, given nothing but visual input streams no more advanced than are available with existing optics, could a car autopilot not be just as good a driver as a human?
I'd advise others on this thread, who tend to dismiss the difficulty of vision, to realize that techniques in ML and Deep-learning are very problem specific and not the holy grail of AGI they're made out to be.
That's a better, but still weak sensor suite. It's probably enough for freeway driving under good conditions. It's far below Google's sensor suite. Or Volvo's.
Now they just have to write software smart enough to not plow into stationary vehicles on the shoulder. There are videos of three separate Tesla crashes where the Tesla plowed into a vehicle partially blocking a lane.
There have been several announcements of low-cost solid-state LIDAR units for automotive. Quantergy announced last year, but didn't ship.[1] Innoviz announced this year to ship in 2018.[2] Advanced Scientific Concepts can't get their costs down.[3] (They have a great unit that costs $100K; the Dragon spacecraft uses it during docking.) Those are all-solid-state devices. There are also some companies trying to use MEMS mirrors, like TV projectors. Eventually somebody will get 3D LIDAR technology working at a low price point, but it hasn't happened yet.
[1] http://www.quanergy.com/products/ [2] http://spectrum.ieee.org/cars-that-think/transportation/sens... [3] http://www.advancedscientificconcepts.com/applications/autom...