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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.
you're funny, the list of things computers can't do with any amount of sensors, that humans can, is quite long
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.
Post may contain irony: discontinue use if experiencing mood swings, nausea or elevated blood pressure.
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'."
Also, we crash more often than is permissible to a self-driving vehicle.
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
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.
No. there is an addtional pair of sensors that are critical- especially to accident avoidance.
The ears.
Nope. Deaf people can drive, and they have no higher rate of accidents than non-deaf people.
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
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.
Good lidar systems see much, much better than camera based systems in adverse weather. I work on FMCW lidar systems and I recall driving to work one day where the fog was so bad that I couldn't even find the road to my office. Once I got work, I turned on the lidar system I was working on and it imaged a building 100 meters away without issue. Road lines are trivial to identify in a lidar system since they have much different reflectivity than the road surface. Objects are also easier to identify because you aren't trying to pull three dimensional information out of two dimensional images. On an FMCW lidar system, you also get doppler information for free. You don't have to try and decide if an object is moving towards or away from you by comparing subsequent images. Every single point in the point cloud includes a meters/second doppler value.
I have to assume your familiarity is with those awful spinning Velodyne systems. They are utter garbage. No self driving car company that I've interacted with is even vaguely entertaining the idea of using them in a production car. They don't even really like using them in their mule cars but, until very recently, they were the only real option available.
The real problem with lidar is that people aren't good at consuming lidar data yet. Once they start to get some experience with it, I have zero doubt that lidar will be the primary sensor on the car. It's the only way you can really build a model of your surroundings with high accuracy, high refresh rate and high tolerance to ambient conditions. So, I actually agree with the GM guy here: Tesla is full of shit. They aren't going to make a level 5 autonomous car with cameras and radar.