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GM Exec Says Elon Musk's Self-Driving Car Claims Are 'Full of Crap' (smh.com.au)
An anonymous reader quotes the Sydney Morning Herald:
Billionaire entrepreneur Elon Musk's claims about the self-driving capabilities of his upcoming Tesla vehicles are "full of crap", General Motors' self-driving Tsar says... "To think you can see everything you need for a level five autonomous car [full self-driving] with cameras and radar, I don't know how you do that"... GM's own solution involves several radar and Lidar sensors, as well as cameras and multiple redundancy systems. Each system costs hundreds of thousands of dollars, and GM are some way away from getting the cost low enough to be commercially viable. "The level of technology and knowing what it takes to do the mission, to say you can be a full level five with just cameras and radars is not physically possible," Mr Miller said.
I don't possess radar, LiDAR, or a gazillion redundancy systems. Stereo cameras (eyes) on a pivoting head and two directional microphones (ears). My software is way better than GM's, though, and I'm expecting Tesla's is too.
Then why can't computers. We have an advantage that we can fill missing pieces in stereoscopic vision to complete our perception and it is incredibly difficult for algorithms to do that. Radar makes up for that, but it is simply possible that Tesla is closer to that then GM is.
I'm guessing Tesla really can't do it well enough, cheap enough either ... yet.
But one of the advantages of having a Bond villain as chairman and CEO is that he's a little less bound by quarterly profit targets and the need to dole out healthy shareholder dividends like clockwork.
For the first fifteen years after Microsoft went public it never paid a penny in dividends. Investors didn't expect dividends; they expected all the profits to be plowed back into world domination.
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Then so can a computer. Just need the right computer and software behind the cameras.
Mike @ The Geek Pub. Let's Make Stuff!
But to clarify the difference:
LIDAR: Formerly about $75k, now about $7,5k per unit, and requires a bubble dome on top of the vehicle. GM and Waymo use it, Tesla doesn't. In addition to looking weird and adding drag, the price is killer if you want to include something on every vehicle. Beyond this, LIDAR doesn't work in fog, heavy rain, snow, and other conditions that humans can drive in - meaning that you'd have to either prevent trips during these conditions, require humans to drive during them, find workarounds (not easy), or rely instead on other sensors. And you still need to understand the world around you visually - LIDAR will tell you that "something" is there, but it can't read signs, see road markings, see brake lights, tell if that thing in the road is a person or a paper bag, etc.
Tesla, for these reasons, ruled out LIDAR. They just simply use the "other sensors" - 1x radar, many cameras, many ultrasonic sensors - all the time. This way, all of their sensors can be put in all of their vehicles, and do double duty for both self driving and standard safety features (autobraking, etc), depending on what options the buyer has paid for. This however comes at a penality: when LIDAR works, it works really well. Photogrammetry with cameras is prone to stitching errors, and radar, while being able to see some things that humans can't, sees the world in very strange ways (for example, a piece of plywood is transparent, but an aluminum can glows like it's on fire). It's a much more challenging task if you leave LIDAR out of the loop. But, it gives you a more saleable product.
In the end, I expect a convergence to take hold. An interesting new technology for example is time-of-flight cameras - they function as normal cameras, but also can read the length of time it takes for a laser pulse to return on every pixel they record. So no dome, just your normal camera coverage and a few cheap, fixed lasers - in mass production, it might not cost much more than cameras alone. In such a case, I'd expect the LIDAR groups to simply replace their conventional LIDAR datastream with the time-of-flight datastreams, while I'd expect the non-LIDAR groups to replace their photogrammetry-and-radar built 3d models with time-of-flight 3d models. But both sides will still need image processing, so it's important to work on maturing that technology today.
That said, let me reiterate that I'm a pessimist regardless of what tech you use. There's just so much nuance in driving in hazardous conditions - understanding when, where and how much you have to slow down, what's safe to drive on and what's not, what things to the side of the road are hazardous and what aren't, when you should break rules (such as driving in the middle of the road when conditions are dangerous but oncoming traffic is rare), what are the consequences of a mistake in one location vs. another, etc, etc. On my gravel road, there's a canyon to one side with no guardrail, and varying amounts of ice and potholes in different places. You better well know how your traction is going to fare as you move across the potholes (vibrating the car and making it lose traction) or icing if you don't want to end up in an unrecoverable slide into a ravine.
Just to pick a random example among countless things that you have to take into account: how long do you think before any self-driving systems will have "sheep recognizing algorithms"? Because where I live, there's sheep. Group of sheep on one side of the road: probably safe. Group of sheep on both sides of the road: not as safe, but probably safe. Lamb on one side, ewe on the other? Very dangerous - the lamb will invariably run to its mother as you approach. Where's the ewe-lamb-running algorithm?
"If there was an antonym to 'Elon Musk', it would be 'Richard Branson'."
According to Google (who's a big LIDAR proponent), it's still $7,5k per unit. It still messes up your aerodynamics and looks dorky. It still can't see in adverse weather conditions, meaning you have to have developed an optical / radar based world-modeling system anyway. And you have to have image processing regardless to read signs, road lines, identify objects, see brake lights, and so forth.
There's real hope for further improvements in LIDAR and its variants in the future, however. We'll see where it goes.
"If there was an antonym to 'Elon Musk', it would be 'Richard Branson'."
Also, we crash more often than is permissible to a self-driving vehicle.
Next you're gonna be telling me how we're not all going to have villas on Mars by the end of the next decade, at the very latest, all brought to you by SpaceX brand rocket ships.
Elon gets my respect for making two successful and innovating businesses that have lasted and have solid fundamentals into the future. He gets no credit for his bullshit factory. Good bullshit has to be believable. Self-driving cars and 800 mph trains in a tube by next year doesn't pass the giggle test.
No, that is not what he is saying. What he is saying is that GM cannot do it with these limitations and there is very good reason to believe that others cannot do it either. As Musk is full of it in a number of topics, it would not surprise me one bit if he were on this too.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
> GM can't do it.
Agreed. But if Tesla can't do it either, he's afraid that Tesla will "poisons the pool" by raising expectations about what can be done and at what price point to impossible levels.
There's been more than one industry that simply doesn't exist at all because people have been trained to believe that anything less than the impossible is either no good or unfairly expensive.
As the head of GM team, he's petrified of Tesla failing but in doing so, sowing the whole field of autonomous vehicles with salt.
On the other hand, expecting Tesla to put the good of the field before it's own welfare is pretty much dreaming and taking shots at Tesla is simply counter-productive. His job is to just grin and bear it.
A person gets all the information about where he car is and has to go based on two sensors (basically two cameras with stereoscopic vision) in your head, positioned sub-optimally inside the vehicle, with one point of view at any one time, and sensors for speed. If you include the persons ass, throw in a cheap accelerometer too.
There is no reason to think cameras and an accelerometer canâ(TM)t figure it out with software to the same degree. But the cars cameras have better vantage points, near perfect operation once the software comes around, and will emphatically understand the rules that govern the roadways better than we could, as well as the dynamics and limitations of the vehicle it is operating. Cars can absolutely get autonomous with less than GM claims.
The human brain has a huge amount of computational power compared with the processors in a car. And typically you're not allowed to drive a car until you're a teenager. So, that brain has 15 or more years of training in identifying all those objects you see while driving. Chances are good that the brain has been sitting in a car many times over those years and gotten good at identifying them at speed.
It's definitely possible that we'll eventually get sensors that can do that, but it's naive to suggest that we're anywhere near that point. The sensor arrays can easily miss a bicycle or motorcycle if it's positioned in the wrong part of the road. With sensors that actually cover the entire lane ahead far enough to cover the stopping distance, it wouldn't be much of an issue, but most vehicles have far too few beams for that to happen. They have gaping holes right ahead of the vehicle that wouldn't exist for a driver. Drivers mostly can't see the couple feet ahead and behind, which are only an issue when going slowly. At speed, you wouldn't be able to stop quickly enough to care about that.
It's not a simple matter of can or can't do. The problem is there's no standard threshold of success which needs to be met for a system to be considered a "marketable" autonomous car. If your car can handle 95% of situations, is it suitable for use on the road and for sale to the public? 99%? 99.999%? Or maybe the proper metric isn't situations, maybe it should be average time in operation before it encounters a situation which stumps it. Should that standard be 1000 hours (6 weeks)? 10,000 hours (a bit over a year)? A million hours (over 100 years)?
Without some sort of standard, you can put a brick on the accelerator and a bungee cord on the steering wheel, and call it an autonomous car. Because it is, for about 20 seconds before it drifts into the next lane. It sounds like GM is working to a much more stringent internal standard for autonomy than Tesla, and the GM exec is frustrated that the press is constantly comparing them as if they were equals. Whether or not the car can drive autonomously isn't as important nor relevant as how often it fails to drive autonomously.
All you people who love government regulation should be all over this, instead of giving Tesla a free pass just because they're Tesla. It's why we have nutrition labels, Energy Star labels, NHTSA crash safety tests, EPA mileage ratings, standardized health plans under the ACA, etc. So buyers can easily compare products on a like-for-like basis
He had the requisite knowledge and resources to invent.
Knowledge how to run a business in a way to recruit and retain engineering talent?
The definition isn't pedantic by the way. The GP used two different words for a reason. Both words exist for a reason. Inventors make physical things, Visionaries use ideas to change markets. Not being an inventor doesn't make you a dummy, and being a genius doesn't make you an inventor.
This trend goes all the way back to the 70s where Woz was the inventor and Jobs the visionary. e.g. Woz created a version of pong. Jobs was the one who took it to Atari. Woz invented the Apple I, Jobs is the one who figured out how to sell it. Woz developed the Apple II system, Jobs ensured that it looked marketable.
Jobs is a visionary. Frankly that in itself is far more important and amazing than being an inventor. Any clever schmuck can be an inventor.
If you could somehow drop a human driver into the driver's seat in the middle of a journey with no prior knowledge, you would find them mostly non-functional. Even if they avoid panic, they would take seconds to minutes to bootstrap their knowledge to the point where they could effectively take over. The closest practical example I can think of to this scenario is how pilots are trained to deal with visual and inertial disorientation, and how they have to essentially troubleshoot their way back to an understanding of their situation. This doesn't happen in milliseconds, and in fact can take more time than is available as we've seen from well publicized air disasters. One can argue that the high rate of accidents from drunk and distracted drivers is due to similar disruption of situational awareness and an inability to recover with just two eyes and an ass in the seat.
A human driver relies on their mind much more than their sensors. Their mind builds a very elaborate world model based on sensory data over time and their understanding of where they are, what they have been doing, and what they expect to be the rules of the environment. The situational awareness that we "see" as a 3D world lit up around us is mostly a construct of our minds showing us our memory and our expectations. This includes not only some physical simulation and prediction (e.g. instincts about momentum and continuity of trajectories of objects in the scene) but psychological and social simulation (e.g. assuming intentions of other drivers and pedestrians and reading "body language" cues). Today's infatuation with neural nets and "deep learning" does nothing to tell us how to construct a synthetic mind with these sorts of abilities which are necessary to compensate for our paltry sensors.
This is the fundamental disconnect. A traditional engineer will think about how much data he needs from a sensor suite to reliably assess the scene with live data combined with the very primitive state model he knows exists in his automation system. A true believer in near-term AI will wave away the vast gulf between current technology and the human mind which we all take for granted every day, assuming that somehow the system can perform as well as us (or better!). More concerning, we have no reason to assume that a futuristic automated system made complex enough to emulate these functions of the human mind won't also be subject to analogous failure modes like confusion, delusion, hallucination, and even antipathy.
The human brain has a huge amount of computational power compared with the processors in a car.
Indeed, and it needs that computational power to do many things that we can get rid of by reformulating the problem. Instead of having a robot's computer do the calculations necessary for bipedal dynamics, you can just give the robot a few wheels. Problem simplified! Assuming that the car would need to approach human cognitive capacity is not justified.
Ezekiel 23:20
Elon's had a long history of proving naysayers like this wrong. My money's (literally) on him to pull this off. The folks truly terrified of self-driving cars are the National Auto Dealers Association (NADA) who stand to lose the most when you can order a car online and have it deliver itself.
The issue is that computer vision doesn't work the same way as human vision. Human's are good at recognizing when things don't make sense, or spotting objects that are partially obscured and recognizing what they are. Humans know that when they can't see most of that thing because of the blinding sunlight reflecting off it, it's a car. The human eye has really good dynamic range too, and a built in self-cleaning system.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC