Camera crushes Lidar, claims startup
spectrum.ieee.org
spectrum.ieee.org
Isn't this the opposite of realistic?
> In broad daylight, NoDar’s setup generated 40 million 3D data points per second compared to the lidar’s 600,000. In extremely heavy rain the number of valid data points dropped by only around 30 percent, while for lidar the drop was roughly 60 percent. And in fog with visibility of roughly 45 meters they found that 70 percent of their distance measurements were still accurate, compared to just 20 percent for lidar.
These results sound very cherry picked.
Also not sure why they picked a 600k points per second lidar instead of a 3 million point per second one like an Ouster 128 beam one.
I did my PhD on stereo and LIDAR; also in industry building these things. This is something that really annoyed us about camera companies selling stereo systems: you cannot tell anything from a colormapped depth plot. It doesn't matter if it's grayscale or jet or a nice perceptually uniform one. Zed are really bad for this in their promo material.
Small errors might be really significant for reconstruction, but depth maps make it easy to hide errors, fuzzy bits, holes, discontinuities etc. Really you want to test against a known calibration object at a distance and present the reconstruction error, show the 3D reconstruction top-down (or in a way which lets you see how much depth variation there is) or compare to a simultaneous LIDAR capture which might be sparse but will be more accurate (absolute) at distance.
I've been just looking at the depth maps to try and figure stuff like height accuracy, minimum separation between objects before they blob together, etc, but I'd like to know your thoughts on what a good method of comparison might be.
The cameras usually aren't the problem. You just need two reasonably high quality machine vision cameras that are ideally hardware synced. There are geometric limits on how accurate you can be, related to the camera separation/baseline and how well you expect you can match at the sub pixel level. 0.1-0.25 px would be considered decent. Normally you'd design the problem in reverse eg what error do we need at the worst distance, field of view dictates lenses, etc. It can be very bespoke.
Depth reconstruction is more reliant on matching algorithms and illumination. You can test stereo algorithms on benchmark datasets (The classics are Middlebury and KITTI). Illumination includes things like random dot projection or other artificial texture to aid matching + reconstruction.
They were testing to disprove the idea it doesn't need ambient light. It's 'worst case scenario / point making testing' not realistic real world testing.
Also I'm not sure I see how you're deducing 'very cherry picked' data there. They've given a range of situations and some general results that look pretty good. I'd assume it's cherry picked in as much as it's not a full data set, but it doesn't exactly yell suspiciously missing anything core to me. Are you expecting the system to fall apart in light rain, but excel in heavy?
You really want a sensor fusion strategy for devices making life-or-death decisions on your behalf.
If you somehow do sensor fusion perfectly and the models never disagree, then why even have the LiDAR? At that point you’ve solved vision using cameras.
Each sensor is given an uncertainty model, that is for example a stochastic model by adding e.g. Gaussian noise to the "true" value it is measuring. Further, you have another model that describes the dynamics, e.g. equations from physics can tell you where the car will go if you know the current speed, position, etc..
1. The kalman filter computes a probabilistic prediction of what it thinks is happening by using the dynamics model. That is, based on what it knows so far, where will the car (probably) be when the next measurement comes up?
2. When measurements from various sensors come in, the Kalman filter uses Bayes's theorem to compute a mean (posterior), in which each measurement is weighted by the probability that the measured value is correct (using the uncertainty models; "correct" here means "in agreement with the prediction"). In other words, sensor that are inaccurate (large variance) will be considered less in the computation of the mean, while more accurate sensors are given more importance.
Once the mean of the measured quantities are computed they are used again in step 1 and the whole thing is repeated. As you can see, that disagreements are justified by the inaccuracies of the sensors, and the process of performing a probabilistic weighted average solves the problem. For the Kalman filter in particular, it can be be show (mathematically proved) that this process minimizes the variance (uncertainty) of the measured quantities (which btw. is an amazing result if you think about it).
A simple example of why this gets complicated: if I have a point in one camera and a point in another camera and I know they correspond to the same real-world spatial point, I can calculate some distance, and the statistics of that calculation can be captured by a halfway-reasonable error model. But how did I know they corresponded to the same point in the first place? Well, because they look the same according to some image feature... or because some deep neural network told me so.. etc. There just aren't very good ways to model just how haywire ^that^ process can go. So at the end of the day, once you let this evil into your perception system, using statistics to blend your sensors together is undermined, and all of your precious covariances just turn into tuning knobs you can twiddle.
The dirty secret is that almost all robotic perception systems are hiding unprincipled, un-modelled heuristics in the data association process. This is kicked under the rug because it doesn't really fit into traditional estimation theoretic frameworks. In a lot of papers you'll see academics push it aside by just calling that the "front-end", which they brush aside as a little widget you put on the front. If you're lucky they'll do ablations across a couple different options.
Of course, this is just one level deeper down the iceberg. It goes far deeper. Even if you could model the statistics of a depth camera well, the statistics of "what are all the objects in your scene about to do" is another couple of orders of magnitude more un-modellable. Often engineers will do something like attach a "constant-velocity" model to the agents in a scene. Imagine trying to bin all of the reasons you might stop walking in a straight line into a bubble that describes how "noisy" that picture of the world is! Now you can begin to appreciate just how hopeless it is to explicitly model uncertainty in the world around us.
You can start to add unjustified assumptions and that'll make the world model weaker, yes. But starting with pretty basic assumptions like "you can segment an object from an series of images because each solid object will move on its own trajectory", or even more basic like "objects have edges" and then a few dozen samples per second, and suddenly you have a fairly robust way to detect things.
Same for predicting where something will go. If you can estimate an objects current velocity, acceleration, and jerk with reasonable precision, you don't really need a highly predictive heuristic for the world model.
For decision making you need more robust heuristics, like "the car to my left has right of way at the stop sign", but you don't need that level of heuristic to identify that there is a car and that it isn't part of the pavement and that it is currently sitting still.
However, you should also consider that working with heuristics is pretty much all engineers do. We ought to solve problems, even if the theory is not there yet. A great example is how we got air travel long before we had any real understanding of the fluid dynamics happening around the fuselage (as it was generally computationally intractable). So, sometimes a simple epipolar camera model with noisy clouds around the subjects is sufficiently accurate for the task. The real problem IMHO is that the degree to which these rudimentary approximations are tested for safety is not nearly enough with respect to how critical they are in the whole system.
A while ago I stumbled upon this presentation on system safety [1] which had an interesting perspective coming from the aerospace industry. In aerospace they test the shit out of every component to make sure that a failure does not cause an airplane to crash. In comparison waymo, uber and everyone else has done almost nothing in terms of testing for safety before putting out their products.
[1]: Richard Murray: "Can We Really Use Machine Learning in Safety Critical Systems?" https://youtu.be/Wi8Y---ce28?si=HsqgiLngdHojpYO9
On technical level two sensors are clearly better than one even if you just pick one in case of disagreement, but as others have said Kalman filters and other more advanced techniques exist. There is a reason airplanes or spacecraft have multiple redundant sensors like this for decades.
The argument only makes sense if you want to save money, but then say you are being cheap up front.
It is indeed Tesla marketing that posits otherwise, which is wrong, and Tesla fans eat it up.
Somehow everyone else has figured it out, and even Tesla knows how to do it and have done it for years.
It’s pure bunk that’s used to cover for other decisions and now gets parroted around
I own a Tesla but I also acknowledge that Elon will claw every last dollar he can to increase margins even by a few cents.
Can you explain this? If you always pick the same one in case of disagreement, what is the purpose of the other sensor? You're not getting any additional information when they agree.
We are perfectly capable combining both inputs and act accordingly. An AI system can easily learn how to combining visual and LIDAR input to make the right decision given the circumstances.In the end it is just a decision tree.
Same as if I'm driving in winter and hit a patch of ice - Visually, it looks identical to the rest of the snowy, icy roads here, but I will drive if I feel and here that I'm spinning my wheels or sliding sideways.
If I smell coolant, oil, or a belt, even if my eyes tell me my gauges disagree I'll be pulling over for those issues as well.
Conversely, if I've replaced a tire and the TPMS light says the removed tire in the cargo area has low pressure (duh, that's why I changed it) but I know the spare (without a TPMS transmitter installed) is good, or otherwise know that the idiot light is a false positive, I'll trust my other senses over my eyes.
* Screeching breaks from the right
* Sounds of a rattling bicycle trying to undertake you
* An approaching emergency vehicle
* Other cars beeping their horn at you
Of course you will confirm such situations visually, but you definitely using hearing in addition to sight.
It did get me thinking about different senses and how I prioritize them. Given that humans strongest sense is visual, it's interesting to me that the priorities of what to trust seem opposite of my expectations. It seems to me that visual signals are seemingly least "trusted" compared to the other senses. As if the logic goes, "my sense of smell is so bad that if it detects danger, it must be very dangerous".
Similarly, I'm sure dogs can smell rotting flesh long before meat is unsafe.
In the context of vehicular autonomy it's more complicated than that because for example a radar sensor picking up an overhead sign or a truck parked on the shoulder as if it's an obstruction on the road is something you want to ignore when you're going 80 MPH on the highway rather than slamming on the brakes.
When you're the only vehicle on the road, stopping if anything goes wrong is always the safest idea. When you're one of hundreds of vehicles in a high speed flow of traffic stopping would put you and everyone else on the road at significantly greater risk.
Imagine a stop light with a lidar sensor that broadcasts that information. Car ahead broadcasting that it's stopped in the fast lane.
They're hardly worth discussing as serious solutions today.
There are two cameras in this computer vision system. If one of them goes offline, is obscured, becomes dirty, or malfunctions you lose stereo vision and depth perception. So you'd obviously need 3+ cameras. And now we're right back at sensor fusion challenges again.
If you hear a sound but see nothing, you do become much more aware of the generic area afterwards, something like that should also be possible.
I can almost see the objection that multiple cameras are as good as one camera+lidar, but I think it's a mistake to trust any system that can't check itself for consistency across multiple bands. It doesn't take a very good radar to keep you from ramming a fire truck. In fact, whatever runs the cruise control's distance sensor should have been enough to prevent a bunch of the Tesla oopsies reported in the press. When tackling one of the hardest engineering problems faced by humankind, it seems stupid not to take advantage of all the data you can get.
Like one sensor says you have a bus stopped in front of you and the other says it's all clear? And your choices are full steam ahead or prepare to not ram the apparent bus?
That being said, even automobiles make safety tradeoffs for cheapness or feasibility. However, we really shouldn't allow any tradeoffs for a completely unnecessary feature like "self driving". Imagine if wanting your car to have android auto or similar meant it couldn't use the lights, because a tradeoff was made.
In a life or death situation, you should opt for the system which will keep you alive more, not the one that costs less.
That’s probably also the cheapest viable strategy.
If a sensor-fusion car cuts accidents per mile (vs human) by 100x, but can only be deployed on 100,000 cars a year, and a camera-only car kills 10x more than that per mile, but can be put on 10,000,000 cars a year for the same cost, the camera-only car will end up saving 10x more people than the “better” system.
(I exaggerated both the improvement ratio and cost ratio because I like multiplying by powers of ten)
Going for absurd safety standards or expectations is absurd and self defeating. Again, as the other anon said, a practical solution that helps improve safety without handwaving material realities (cost, feasibility, adoption rates) is always better than a "safer" option that won't actually be used.
Obviously corporations try to make more money, but people also dont like buying more expensive cars.
A calculation that leads you to underdesign a product's safety and leaves no room for this product's safety improvement, in terms of mechanical or electronic update, is clearly not thought as being safe in that regard, regardless of the economies of scale or even low-term utilitarian goals (that would be expressed as: people spending money on a tesla would be safer in the short run, rather than using no automatic driving at all while waiting for a better product).
This is an important difference, and there is a societal choice to make here: do we (as society) want to buy now, and potentially have regrets later (when the safety of the product degrades with time, causing it to also have a record of people's deaths), or do we want to proactively force a notion of safety onto cars that is more than just being good enough at an arbitrary point in time, so that we hav more confidence over the long-term viability of that (societal) investment? As you can guess I gravitate towards the later, but of course it's a gradient, with several choices in-between, because pushing that thinking to an extreme would lead to stagnation, which would not do anything in terms of improving safety, as you noted.
If the outcome of your safety discussion ends up suggesting a “safer”, “more expensive” solution that will definitely leave more people dead and injured then that analysis, then something is seriously wrong.
There is no robust proof that any self-driving system outperformes a well-trained driver.
We could take that money and invest it into advanced driving lessons
As far as LIDAR itself: sure, yeah, you get depth info out of it. But depth info is only part of the problem, and frankly it's clear at this point that it's one of the easiest. The hard parts are in the recognition side: not "is that pedestrian in your path" (easy), but "is that pedestrian going to step into the street or not" (hard). And that's a computer vision problem. You can use LIDAR output as vision input, sure, but it's has no advantages.
Tesla was right, basically.
That's proven false by the cars continuing to drive into stationary objects. This failure mode is not ambiguous.
Camera input is garbage for interpreting geometry, especially from very smooth or very discontinuous surfaces, and especially with the shitty low resolution and low dynamic range cameras they use, and especially with non-stereoscopic cameras with no motion freedom relative to the vehicle body. Lidar is a necessary crutch for working around the fact that, while hypothetical cameras that don't exist might work well, all available cameras are unsuitable for the purpose, and calculating multi-view geometry accurately costs time.
> The hard parts are in the recognition side: not "is that pedestrian in your path" (easy), but "is that pedestrian going to step into the street or not" (hard). And that's a computer vision problem.
Pedestrian motion is not strictly a vision challenge but a general category of environment understanding (mass, momentum, motion mechanics). Vision is only one possible input mode preliminary to modeling.
It has? This again gets to "are they safer than human drivers?", because the competition hits stationary objects all the time. If you have data let's discuss data, but "proven false" is, again, just spin.
> Pedestrian motion is not strictly a vision challenge but a general category of environment understanding
Semantic evasion. You agree that it's "not a problem solved by LIDAR", right? It needs a camera. You can use a LIDAR output as a (somewhat inferior) camera, but it's not providing any advantages.
It looks like you're jumping from "depth is easy with cameras" (demonstrated false) to "safer than humans anyway without it" (speculative and not demonstrated by anyone), so who here is evading? That they're safer is not demonstrated. That depth is easy with just cameras is demonstrated to be false by the continuing failures in the presence of extreme financial incentive to not have those failures.
> You agree that it's "not a problem solved by LIDAR", right? It needs a camera. You can use a LIDAR output as a (somewhat inferior) camera, but it's not providing any advantages.
The LIDAR addresses the part where all current cameras are unsuited to mapping physical world geometry under driving conditions. It's not one or the other, but you appear to be assuming an imaginary not-the-one-we-live-in reality where only one is needed because you assume that all available cameras aren't actually very bad. But they are all actually very bad. So we continue to need both for the indeterminate future until someone invents mechanically robust extreme fidelity stereoptic cameras with motion freedom independent from the vehicle body, which is what humans use.
Humans are unsafe predominantly because of inattention, not ability. Camera-only vehicles are unsafe because of camera ability before you even get to the attention part.
Tesla's repeated failures over the years (and your conviction toward what Tesla is doing regardless) demonstrate a dangerously erroneous belief that object identification is the first and most important step for path planning. But that's not how humans drive, and it's not how to drive safely. The vehicle should avoid driving into any space that isn't going to be open smooth road, period, so the most important step is mapping geometry. There are no cameras currently suited for that. This is not a theoretical limitation. Just a practical one. Becoming suitable with current cameras would require many more cameras with much more processing per frame, so if you're trying to save costs vs lidar, you won't.
humans are absolutely doing sensor fusion, brains are bayesian inference machines. do not underestimate the power of the visual system.
and no, that the brain does is not an argument in favor of LIDAR-less cars. the eyeball + visual cortex system is alien technology compared to our feeble models. beware the hubris of a man who has learned to classify golden retrievers.
Not in the sense in the upthread comment they aren't, no. We have two cameras and two microphones. The latter is limited to weak detection of horns and tire screeches and not much else, and the former are too close together to give stereoscopic depth information at traffic distances.
We have a camera, basically. We do lots of stuff with the camera, sure. But that's not sensor fusion.
We sure as hell don't have anything like LIDAR.
They're only too close together to give very precise depth information, but they do still provide useful depth information. They also double the incoming light and SNR, which is why the average person performs better on visual acuity tests with both eyes than with only one or the other.
> We have a camera, basically. We do lots of stuff with the camera, sure. But that's not sensor fusion.
They're varifocal cameras with very good dynamic range that receive double the light input and that also have full freedom to move around, both rotationally and translationally, which provides, among other things, more depth information and better object boundary segmentation from controlled parallax and focus, and which involves the continuously varied activation of many different muscles and sensory nerves because they're attached to the extremely complex and sensitive proprioceptive structure called the rest of your body, which your brain fully uses as input when processing visual information. And we know that your brain uses this other information, because not having this other information causes reduced perception and motion sickness.
So, no, they're not just cameras. And, yes, we do sensor fusion.
Sonar sensors are most accurate at medium ranges, but they are notorious for detecting ghost objects that do not really exist. Infrared range sensors are more reliable but are only accurate at very short range. So when a sonar sensor detects an object 8.4 meters away, you use the infrared sensor to double check. If the infrared sensor says there's an object 9 meters away in the same direction, you assume the object is real but is actually 8.4 meters away. If the infrared sensor says the nearest object in that direction is 20 meters away, you assume the sonar sensor made something up.
If you have enough types of sensors, you can also use a "majority rule". If two of 3 sensor types agree, you assume the 3rd is an anomaly. Lidar is excellent for this because it is accurate across a very large range, so it tends to overlap with most of your other sensors. This increases that odds that when there is a disagreement, one of the agreeing sensors will be capable of accurately measuring the distance to the object.
Do AI systems have the potential to weight or inform those transactions based on historical historical data then? The “experienced” aspect of learning all the things that turned out to be true or false in previous comparisons or data decision points would seem to be the obvious missing piece, but I have never really understood the specifics.
Maybe the bigger question - anyone know the status of low cost lidar? Dozens of startups and larger companies were working on it 10 years ago, yet Lidar still costs “thousands” according to the article
Hobbyist buying a few units of a component, even if they are buying it with a significant margin, will net the producer peanuts. So not surprising they don’t worry much about serving them that market.
In my case I was looking at buying quite a number of units, outside of a hobbyist application. In fact, I would say it was a higher number than the cheaper China-made products could possibly sell (different market sizes). It seemed to me that they didn't want to sell for any price really but would make an exception if they could really, really rip me off.
1d lidars that have a range of 8 meters indoors are quite cheap <$15 volume.
"2d" lidar, that is one measuring one plane's depth, are generally a lost more costly. Not only that they are bigger and eat more power. again indoor only.
3d lidars are more expensive still, and if you want it to work outdoors, even more.
1. Attempt to use the vehicle's built-in windscreen wiper to remove the obstruction.
2. Failing that, stop the car. Preferably before the vision gets so badly obstructed that the car cannot safely be brought to a stop. But stop the car even so.
3. Get out and clear the obstruction. Admittedly the AI will have trouble with this, but it is vanishingly rare anyway, and if the car is carrying passengers, this task can be given to the passengers.
How do we handle the AI mowing over a pedestrian when it makes a bad judgement call? Right now, the status quo is that we do jack and shit, and I can't help but feel like that's not a good plan.
What redundancies can you implement in a black box "AI" model?
People and ADAS have their own, different, and critical weaknesses. Neither is a panacea. (Which is why mass transit investment should be prioritized over scifi fantasy ADAS.)
Humans have something called perception and cognition, we can make sense of things we don't see.
AFAIK we don't have cameras yet that can do that. We need better sensors.
"Solid state" lidars would fit the fit bill for likely low cost lidar. They are probably 3-4 years out, and have been for the last 10 years.
I used to think the more sensors the better, but after listening to George Hotz talk about it I can see the logic of focusing on ambient spectrum in visual and near range. Of course, he will talk up his approach as best, but here it is as best as I recall:
1. more sensors ~= more signal
2. more sensors means
a. longer processing pipeline for fusing data streams (timing, registration)
b. more software, thus more surface area for defects
c. decisions about response when 1 sensor modality fails
3. visual range spectrum is
a. well adapted for environment
b. has inexpensive and high quality sensors
c. sufficient for humans so is sufficient to get to human-like driving by a computer
1. to have protocols to slow down and stop gracefully
2. maintain enough of a spatial model of the vehicle surroundings to perform the above (Simultaneous Localization and Mapping, SLAM)
The book "An Immense World: How Animal Senses Reveal the Hidden Realms Around Us" by Ed Yong [0] is really great for understanding how sensory input informs but isn't the same as a mental model of the world built into the operations of a living thing.
Likewise ADAS and similar systems do not operate simply on what is sensed at any particular moment. Even ahead of things like being blinded by a sunset, there are occlusions when one object moves behind another and cannot be directly detected but can be inferred by an object model that predicts future positions given the the earlier known velocity and acceleration. [1]
0. https://www.amazon.com/Immense-World-Animal-Senses-Reveal-eb...
1. Visual SLAM in dynamic environments based on object detection https://www.sciencedirect.com/science/article/pii/S221491472...
These two processes are actually why VR can be difficult on the eyes, because while the main way your brain senses depth is the parallax (the classic "binocular vision" way people think of), the sense of focus is telling your brain that everything is right in front of your eyes.
Do you have any sources for this being a significant factor in human depth estimation? “Infinity” focus starts at 6 meters, yet we’re able to estimate much larger distances with great accuracy.
Watt's micrometer, designed between 1770 and 1771, was what we would now call a 'rangefinder'. It was used for measuring distances, and was essential for his canal surveying work.
Adapted from a telescope, with adjustable cross-hairs in the eye-piece, it was particularly useful for measuring distances between hills or across water.
0. https://digital.nls.uk/scientists/biographies/james-watt/dis...
1. https://collection.sciencemuseumgroup.org.uk/objects/co59281...
It is nice that the system can generate 40 million 3D data points per second, but those point still need to be processed (and interpreted) in later stage processing.
This makes it very hard and computationally expensive because you need to search progressively larger areas of the image to do matching to get the same FOV at longer range. Large baseline also is more likely to suffer from occlusion (even self-occlusion) by objects in the foreground resulting in holes in the depth map. In addition to being harder to keep the cameras rigid between calibrations (but I guess they figured this part out).
Instead of a depth map like they show it would be much more useful if they instead show a map of the reprojection error of a flat calibration target at the minimum and maximum depth ranges. Even if they are not aiming for geometric accuracy it would give a much better idea of the actual performance of their system.
The problem with these stereo camera companies is they get obsessed with maximizing certain metrics when the fact is there is a fine balance of trade offs to maintain that is highly specific to the desired application and even scene. Sure, you can increase the baseline and figure out how to mitigate calibration but that creates many more problems to solve that only become apparent when you try to apply it to some application.
Anyone interested in long stereo baseline should check out this blog which has been around forever: https://www.elphel.com/blog/2017/09/long-range-multi-view-st...
Does anyone understand this? The problem with fog as I understand it is that it defuses light, washing out vision. My understanding is that Lidar does not bounce off fog, or at least to the same degree.
Is my understanding there correct?
What happens with these sensors? Can they detect physical interference? Will they refuse to let an assist mode of any kind activate if there is a malfunctioning sensor?
What kind of self-diagnostic do they run, how often and how fail-proof is it?
Self driving cars will require regular and stringent inspections of functionality, otherwise it's a time bomb.
* Since safety regulations will be required for this, include a built in self test, with a refusal to engage self driving until the test/sensors are deemed safe enough.
* Race cars use a spool of plastic film over their cameras, so a clean window is always ready. That's on option. There's the standard wiper. If you look around next time you're out, you'll see that most cars are very clean. So, I think this would be a non issue for most people.
* Since the self test requirements may become more stringent over the years, cars that can't comply with the latest requirements can have an anual/semi annual verification, like we do now for emissions of older cars. Maybe as part of the standard, thresholds for the self test will need to be adjustable, to keep them in the "safe" range, and trigger earlier checks/cleaning.
* You have to compare all of this to having a human behind the wheel.
Tesla Model3 is one of the most popular selling cars now and that's just a hop and skip away from someone hacking the self-driving to always being on and selling it for cheap like the exhaust bypass hack that's been done to tens of thousands of trucks on the road today. https://www.thedrive.com/news/inside-the-epas-messy-war-on-d...
It does, in the other parts of the world, where there are cars being made that exist outside of the luxury market.
You can still get a Nissan Versa for $16k. Adjusted for inflation, that's only $7k, in 1990, which is cheaper than a Ford Escort from 1990, and 35% more horsepower.
The problem is that people's standards are ridiculous.
What happens when the windshield gets to coated in junk that the driver can't drive?
In any case, many journalists are still trying to spin it as cameras vs lidar when it's really single modality vision systems with no redundancy versus multi-modal vision systems that conventionally include camera as well as lidar.
Various Lidar startups have also made inroads in deriving vision from laser range finding technology. It's hard to speculate on what will win in the end, but nitpicking these things is a form of bike shedding. Ultimately it comes down to the software, which is a much bigger problem and not as straightforward to wrap one's brain around.
In a single small extremely constrained geofenced area using vehicles with hundreds of thousands of dollars worth of sensors each. Those companies are complete dead ends. They are not even remotely solving for the general case.
And levels 3/4 are useless, better served by public transit. There's a reason the Japanese/Korean manufacturers are skipping them. You either have really good level 2 (i.e. LKAS+ACC), or you go to full level 5. And Waymo/Cruise will never solve for level 5 with their approach. Tesla (and Comma) are at least working on the general solution to it.
Level 4 where the geofence is "all major cities and highways" would be immensely useful.
> There's a reason the Japanese/Korean manufacturers are skipping them.
https://www.caranddriver.com/news/a35729591/honda-legend-lev...
https://canada.autonews.com/technology/hyundai-very-close-ac...
1:32:39 Andrej explains why Tesla removed Radar and relies on vision. Part of it is that other sensors bloat and complicate the software needed to interpret data coming in. What if they get conflicting signals? There may be calibration or manufacturing inconsistency to account for. You need to normalize that in your software and the entropy quickly gets out of control. I find this explanation very compelling and feel like vision is the more "necessary" sensor anyway. After all, roadways are designed to be navigated using vision.
But these days, I’ve learned to wait for a working commercial prototype, before cheering.
Claims never settle anything.