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Contrary to Musk's claim, Lidar has some advantages in Self Driving technology
- dmh2000 7y agoa big drawback is not performance but that most lidars are expensive and fragile.
- MobileVet 7y agoThis was true... until recently when Luminar announced a fairly affordable (for an automobile BOM) ~$1k version to be available this year. https://www.engadget.com/2019/07/12/luminar-affordable-lidar/ https://www.engadget.com/2019/07/12/luminar-affordable-lidar...
- acd10j 7y agoArticle clearly mentions that these will be ready for production not before year 2022, until then it's all speculation.
- qaq 7y agothe draw back for autonomy you need to recognize objects.
- streetcat1 7y agoTo sum up. If I use Lidar - Lidar is good. If I do not use Lidar - Lidar is bad.
- nostrademons 7y agoThat's what each of the companies involved claim, but here you additionally have benchmark results from independent researchers who aren't building self-driving cars that say Lidar is good.
- streetcat1 7y agoEverything that you see is a piece of marketing (in both sides). This "research" was drafted, vetted, reviewed, approved (by at least 5-10 people).
- rumanator 7y agoTo be fair, they picked what they felt was the best option available to them considering their requirements and based on their experience. It would be weird if anyone claimed that they were using bad tech while acknowledging their competitors are doing things right.
- simcop2387 7y agoI wonder about the noise aspect of this when you've got 20 cars nearby also using lidar. Is there a point where these kinds of active sensors begin interfering with eachother? I know that it isn't lidar but the xbox kinect's used to interfere with each other if you had multiple in one room
- ThatGeoGuy 7y agoThat really depends on the modality of the LiDAR. For the record, the Kinect is techically LiDAR since it is using "Light Detection And Ranging." As for why the Kinect interferes with other units, it's because of the imaging modality (structured light). The sensors interfere with one another because they're largely dependent on detecting a specific pattern of projected dots. If you detect too many dots or if the image gets saturated, you start to have a problem. In the case of traditional scanning LiDAR (e.g. terrestrial LiDAR in the sense of a Leica or Faro or Velodyne unit), this isn't necessarily the same case. Sure, if the two lasers point exactly at each other for a given point over their sweep, then at that point the lasers will saturate the measurement and that specific measurement will not be useful. In time-of-flight based, mirrorless systems, this matters less than one might think. I can see this being consistently a problem when scanning with Velodyne tech since they tend to only rotate about one axis, but for other types of LiDAR I don't think it would be as big of a deal. Granted, then you have to worry about scanning speed and how that affects the final results. Overall, I don't think that unit interference is going to be a significant factor in adoption. LiDAR is a broad technology and it's not easy to make assumptions about the entire industry based on a couple implementations or modalities.
- flor1s 7y agoAs an aside, the original Kinect and Kinect for Xbox 360 use different technologies for 3D detection. The original Kinect projects an infrared pattern and then detects the deformation of the pattern to determine distance/shape. The Kinect for Xbox 360 uses more traditional time of flight.
- acd10j 7y ago
- ThatGeoGuy 7y ago> For example, one of the distance estimation algorithms used in the Cornell paper, developed by two researchers at Taiwan's National Chiao Tung University, relied on a pair of cameras and the parallax effect. It compared two images taken from different angles and observed how objects' positions differ between the image—the larger the shift, the closer an object is. The shift or disparity between sensors doesn't really matter. We've known that wider convergence angles begets better object point estimation since the 70s. Yet, even the KITTI dataset doesn't attempt to take advantage of this, and uses two rather average cameras with a (relatively) short baseline of 0.06m (see: http://www.cvlibs.net/datasets/kitti/setup.php http://www.cvlibs.net/datasets/kitti/setup.php). That's 6cm!!! You have the entire width of the car to separate these cameras by. > This technique only works if the software correctly matches a pixel in one image with the corresponding pixel in the other image. If the software gets this wrong, then distance estimates can be wildly off. Again, yeah. But the problem is twofold: you need to detect / match similar points between two images, but the fundamental setup of your system can limit your precision and accuracy. Use a wider-angle lens with better convergent geometry. Every publication based on the KITTI dataset doesn't even address some of the most basic criticisms from photogrammetry. Which leads to probably why LiDAR gives such a distinct advantage in most of these data sets. You solve two problems: 1) You solve the correspondence problem trivially because LiDAR doesn't need to match points between cameras, and there's no baseline / convergence criteria that the final point precision depends on. 2) Robust geometric data is well-modelled, well understood, and provides an easier criteria for machine learning systems (particularly ones running over KITTI, as in the article) to converge on than just using stereo-imagery with a baseline of 6cm. You get the scale of the system for free and your calibration troubles are whisked away as LiDAR systems tend to be better-calibrated and more stable than most lens systems or configurations you'll find in the cheap off-the-shelf cameras that many autonomous driving startups are using. I guess I come off a little negative by looking at this, but my first reaction to Musk saying that nobody should or will want to ever use LiDAR for this is that he doesn't know a damn thing about what he's talking about.
- tlb 7y agoA 6 cm baseline is enough for humans to make adequate distance estimates. Besides the correspondence problem, a longer baseline makes it hard to keep the cameras aligned as the vehicle bounces and flexes. You can't mount them separately to the car -- a chassis can easily twist by a degree or two. So you need a stiff mounting bar between them, which you can either put outside the car like a roof mount (ugly, and it gets buffeted by wind) or inside (also ugly).
- m463 7y agoI agree with Musk and see lidar where ray tracing was decades ago. It was an expensive impractical "holy grail". A set of lidar sensors right now costs as much as a car. Maybe at some point in the future one of these lidar startups will come out with an inexpensive (maybe solid-state) version to augment the current sensors. Or maybe by that time vision will have gotten much better.
- georgeburdell 7y agoThe cost of lidar is going to plummet due to exactly the end of your post. Several startups (SiLC, Aeva, etc.) are using silicon photonic integrated circuits. Several more early stage startups have either mems or phase array prototypes for completely solid state chips.
- kjksf 7y agoThe thing is, self driving wars will likely be over before economies of scale for production of lidars will happen. If non-lidar system doesn't work, then the cost of lidar, even at $10k, is irrelevant. If you can make non-lidar system work better than humans (i.e. with quality acceptable for regulators) before the cost of lidars drops down significantly, then lidars lose based on economics. And the cost of lidar won't drop significantly quickly. The next step-change in price would probably require mass production i.e. production of hundreds of thousands of units per year. Even if lidar robotaxis happen before non-lidar ones, initially they'll be made in tens of thosands of units per year, leaving a couple of years for non-lidar tech to catch up.
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- gauku 7y agoBlickfeld is working on solid-state LIDAR. From what I've heard from friends working there, their sensor is/will be available for under $1000 which is a huge cost reduction from the current price(LIDAR) == price(car) solutions
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- QuantumGood 7y agoMusk's point has always been to combine vision with radar, instead of Lidar. I'm amazed that this combination is usually overlooked in discussions of Tesla/Lidar
- ztjio 7y agoThat wouldn't be very good click bait
- llbowers 7y agoI’m sure this is a dumb question to anyone with knowledge in this field but is there any reason to not use all three together?
- AndrewBissell 7y agoCurrently, LIDAR is very expensive, certainly too expensive to build into every Tesla being manufactured. So Musk would not be able to sell a "full FSD capability" option on his cars if he acknowledged LIDAR is useful/necessary to autonomous driving. The number one link if you search "Tesla" on HN is "All Tesla Cars Being Produced Now Have Full Self-Driving Hardware." It's been an extraordinarily effective marketing gimmick.
- thecupisblue 7y agoLIDAR prices have declined and are going way low-budget now.
- robocat 7y agoFor example these guys suggesting $500: https://www.wired.com/story/lidar-cheap-make-self-driving-reality/ https://www.wired.com/story/lidar-cheap-make-self-driving-re...
- GuB-42 7y agoThere is a $300 vacuum cleaner robot by Xaomi that uses a LIDAR. The part can be found for $70 on AliExpress. Obviously not the same specs as Waymo's LIDAR but at least, it proves that it doesn't have to be that expensive.
- vecplane 7y agoOne of the major disadvantages of LIDAR is poorer performance in rain, snow, and fog, which are quite common in many parts of the world. I'm surprised that isn't being discussed more.
- loourr 7y agoThis author missed Musks point entierly. His argument is that to solve self-driving you need a deep understanding of your surrounding which you can only achieve with visible light spectrum video. That's the real hard problem to solve and you need cameras to solve that and if you solve it then lidar becomes unnecessary. The doomed part is because if companies are spending all of their energy on creating neural nets around lidar then they'll reach a local maximum where they never begin to tackle the much more difficult problem truly needed for self-driving.
- threeseed 7y agoMusk's argument was refuted by his own Data Scientists. They admitted their own models are far from perfect and will likely never be. The concerning one in particular was the "is this a large object" model which initially failed to identity objects such as car-carrying trucks, cranes etc. With Lidar you can certain at least that it will identity an obstacle.
- AndrewBissell 7y ago> His argument is that to solve self-driving you need a deep understanding of your surrounding which you can only achieve with visible light spectrum video. That's the real hard problem to solve Musk's argument is more that cameras should be sufficient because humans can drive using only two eyes to perceive the driving environment. He always neglects to mention that humans do this with a combination of sight and a brain capable of general intelligence. I'm sure it's true that if Tesla invents AGI, self-driving with just cameras will become tractable. But "real hard problem to solve" doesn't begin to capture the difficulty. In reality, since no one has yet invented a self-driving computer, it's impossible to say what components are necessary or even whether there may be more than one way to skin the cat. But one source we should probably take with a grain of salt on this issue is those (like Musk) with an intense commercial interest in one perspective.
- kjksf 7y agoYou could have made the same argument about any hard problem before it was solved. "So this guy says a machine can outplay chess with just a CPU, some memory and a bit of code. He neglects to mention that humans have a brain capable of general intelligence". Year later, computer beats human in chess. "So this guy says you can teach a computer to play Go just by unsupervised training of neural networks. He neglects to mention that humans have a brain capable of general intelligence". Year later, computer beats human in Go. "So this guys says you can program neural network to play computer games competitively using vision and deep learning. He neglects to mention that humans have a brain capable of general intelligence". We don't need AGI for self driving. The difficulty of self driving is probably less than a dog walking on the street. No, a dog doesn't steer a car, because he doesn't have hands, but he's performing the same vision and planning tasks like a human (or AI) driving a car. He knows where he is, he knows where he wants to go and he uses vision and his non-AGI brain to plan a path to get there while also avoiding dynamic, unexpected obstacles.
- georgeburdell 7y agoThe article misses one important point about LiDAR. Frequency modulated variants, referred to as "FMCW", get velocity information for free via the doppler effect. You can't get that information from a camera without sophisticated image processing, and you can't get it with high resolution from RADAR. Knowing velocity as well as position is important to assessing immediate safety threats. There's a good write-up by the co-founder of SiLC, a silicon photonics LiDAR startup, here: https://www.photonics.com/Articles/Integrated_Photonics_Looks_to_Advance_Safety_for/a64791 https://www.photonics.com/Articles/Integrated_Photonics_Look...
- siliconc0w 7y agoAnother possible argument is simply pragmatic - by not including LIDAR, Tesla can actually sell cars and therefore be in the best position to get to L5. They'll have the biggest fleet, the most data, the most technical expertise and experience, etc. I mean they're actually selling something people are buying. It may or may not be the correct technical choice but it seems to be easily the correct business choice.
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- Animats 7y agoWhat gets me is the obsession with "identifying objects". The first thing you want for self-driving is an elevation map of what's ahead. If it's not flat, you don't go there. You don't need to know what it is. This is what LIDAR is good at. Radar is not yet usable for ground profiling. Radar returns from asphalt at an oblique angle just aren't very good. Nor is there enough resolution to see even large potholes. Maybe someday, with terahertz radar. Not yet. Now, you can go beyond that. If you're following the car ahead, and it's moving OK, you can assume that what they just drove over was flat. If the road far ahead looks like the near road, and the elevation map of the near road says it's flat, you can perhaps assume that the road far ahead is flat, too. That's what the Stanford team did in the DARPA Grand Challenge. Identifying objects is mostly for things that move. Either they're moving now, or they're of a type that's likely to move. This is where the real "AI" part comes in - identifying other road users and trying to predict or reasonably guess what they will do. Collision avoidance based on object recognition has not worked well from Tesla. They've hit a street sweeper, a fire truck, a crossing tractor-trailer, a freeway barrier, and some cars stalled on freeways. All big, all nearly stationary. This is the trouble with "identify, then avoid".
- TeMPOraL 7y agoEven when trying to identify objects, I don't get the push for trying to understand what they are. The way I see it (and I might be very wrong here), you should be able to get good enough results by identifying things around you that are solid (that's the most important part), and tracking their velocity. This should be doable without any kind of understanding of what the objects are. Then the car's control system should keep the speed and direction such that the car can always be stopped before any of the tracked objects hit it. Having done that, you can play with identifying lanes of traffic and sidewalks and other "normal" features, and potentially ignoring objects there as long as they behave according to expectations. But I'd think the first order of business would still be ensuring that you don't run into solid objects, whatever they are, and whether or not they're moving.
- miahi 7y agoIdentifying objects means you not only know their current position and speed, you can also anticipate their possible speed and trajectory changes.
- Traster 7y agoI think I would find Musk's claims more compelling if he had actually sat down with an expert and discussed in detail why he believes what he believes. Instead we're sitting here discussing quote a kooky quotes with no real analysis. Even on the face of it >"They're all going to dump lidar," Elon Musk said at an April event We know which companies are building self-driving cars, we know what technologies they're using and we know how long they've been working on it. Have we seen any signs that any of these companies are dumping LIDAR? I would've thought it'd be pretty big news right?
- killjoywashere 7y agoKarpathy is leading the ML team and Musk is no slouch. I'm sure he doesn't write code, but he's been through linear algebra, quantum physics, and statistical mechanics. He understands how photons work, how sensors work, how computers work, how the math works. So he can quickly assess the business utility of a proposed solution, or the plan to find solutions. They have more than a couple guys at this caliber. Every person in that presentation was top-of-their-game, mid-career, I'm-not-falling-on-my-sword-for-some-bullshit.
- coldtea 7y ago>Have we seen any signs that any of these companies are dumping LIDAR? I would've thought it'd be pretty big news right? In fact, why would they not have a multi-faceted system, keeping LIDAR and alternatives?
- CrazyStat 7y agoBecause LIDAR is expensive. If camera-based neural networks eventually get good enough that LIDAR provides minimal additional value, they will drop it. This is what Musk is betting on. We're not there yet for sure, and it's not clear to me yet whether that's a realistic goal for the 5-10 year time frame.
- hef19898 7y agoMusk is betting on a lot of things lately. I would rather have him focus on one or two hard problems and solve these first.
- killjoywashere 7y agoMusk said he is a fan of LIDAR, the Tesla has forward-facing LIDAR, and he even helped develop a LIDAR for the Dragon docking system. This is a hit job.
- Havoc 7y agoSurely a blend would yield the best all weather solution? Even if the lidar isn't top shelf.
- linsomniac 7y agoHow well do Lidar systems work when there are 5, 10, 20, 50 cars with Lidar units all in a small area (like a busier intersection in my smallish town)? I wonder if Musk has done some tests of this sort of scenario and that is what he is basing this judgment on?
- williesleg 7y agoI don't have lidar, I can drive really well.
- cameldrv 7y agoIMO all of the cost and power arguments are currently red herrings. An autonomous car or truck is worth at least $100k more than a non autonomous one, so whatever it takes sensor wise is worth it. Where musk's argument makes sense is that if lidar can't be made to work in all weather, putting effort into making algorithms for it may be a dead end. There are a number of companies with various approaches to making lidar work better in bad weather though