I walk/drive/bike in a city. I'm wondering if we'll ever get to fully autonomous.
The thing is I'm often looking at other drivers and when driving pedestrians look at me. You can send them across a crosswalk you are stopped at in a car with a wave or a tilt of your head. You can tell that person isn't going to cross the street and is waiting for a ride because they are looking at their phone and then cars. Sometimes pedestrians wave me though when I'm in my car.
I had a situation where an adult waved me across at an intersection but her kids kept going. I stopped, kids stopped after mom told them to and we were back to square one of our intersection dance. Things that sensors are going to have a hard time doing.
I think we'll get driver assisted as sensors can look 360 and further ahead. highways and less dense areas seem ideal for self driving.
"Monderman found that the traffic efficiency and safety improved when the street and surrounding public space was redesigned to encourage each person to negotiate their movement directly with others."
I remember reading about some design studies for "car-to-pedestrian communication" a while ago. I think one idea was to project a zebra crossing in front of the car to let pedestrians know it's safe to cross.
I think ideas like that show that there might be ways to solve those problems - but interaction with a self-driving car will likely be very different than interaction with a human-driven one. (And you'd have to teach/convince people to accept this new mode of communication)
In any case I agree it's very early days to even attempt to solve those problems.
(Of course it's possible things will go the easy route and self-driving cars will more be regarded like trains and restricted to special routes strongly off-limits to pedestrians.)
Self driving on highways is already working. There are solutions for interacting with periastrians. Mercedes had a prototype where a virtual cross walk was displayed in front of the car to show it was waiting for the pedestrian.
#1 cause of pedestrian injury right there: car in lane 1 stops and {driver waves at pedestrians/car displays crosswalk}; pedestrians start walking; car in lane 2 doesn't stop.
A protocol incompatibility issue? Oh well, can't make an omelette without killing a few people :-/ In other words, this requires precise cooperation from multiple parties involved; one fancy car implementing this is insufficient.
I have to admit that self driving cars are not better in all circumstances. They don't have to reach that bar for me to prefer them though. If they do better in enough situations to outweigh the situations where they do worse, they should displace non-self driving cars. The biggest advantage I see is that they always perform to their standard, whatever it may be, instead of wildly deviating as humans tend to since humans are often drunk, tired or distracted. Their biggest disadvantage is dealing with real life context clues like you mentioned, or times when the road is different than what the map suggests and you need some common sense.
Just attach lasers to your helmet and you should be able to divert all the autonmous vehicles away from your path. They're pretty easy to fool right now.
Various old people in England have told me about when goods were delivered by horse and cart (milk, bread etc).
The horses knew the route, a man would walk alongside the cart and make the delivery while the horse waited, before proceeding to the next customer's house.
Usually, the purpose of telling me this story has been the time the delivery man was absent; either through a sudden illness or drunkeness. The customer notices the horse stop outside, but then goes to investigate when the horse walks away but there's been no delivery.
1. There are supposedly new lidar systems coming out in the hundreds of dollars.
2. You can be "superhuman" with just image sensors (i.e. without lidar).
That said, I believe lidar is effective for precise object determination e.g. whether a small thing on the road is soft or hard which would help determine if it's a stuffed animal or a real one or a plastic bag in the shape of an animal.
I really do not believe lidar is going to be the right answer for self driving cars. We already know that you can be successful at driving a car in various weather conditions with two stereoscopic cameras on a swivel and limited sonar (humans). Cameras, infrared cameras, and sonar will likely be much cheaper and just as capable as lidar.
If you think humans are "two stereoscopic cameras and a limited sonar", you need to learn more about humans.
Not only are our eyes far more versatile than the most sophisticated camera equipment, we constantly maintain a mental
model of the world that allows us to focus only on the things that matter, and accurately infer state. This affects everything from
vision, to hearing, to proprioception and situational awareness.
Machines don't come close, which is why the quality of the sensors matters so much more. This trope gets repeated in nearly every thread about self-driving cars, and reveals mainly that engineers don't know
much about biology.
Our eyes may be versatile, but I've never heard of a man-made camera that comes equipped with a sophisticated image regeneration algorithm because the film/CCD has a giant black hole about 1/3 from the center.
(...And now imagine it also comes with an automatic jitter motion generator and sophisticated time-differential logic because the sensor zeros out if exposed to the same color for ten seconds.)
We have terrible sensors. We just evolved to work around the problems.
That's no different from the problems you need to engineer around in a CCD or CMOS - stuff like dark current, shot noise, read noise, RGB filters effectively dividing resolution by 3, an upper limit on integration time which is almost the same as "sensor zeroes out if exposed to the same color for ten seconds".
OTOH, the eye's selective resolution and instant slew+focus of the high resolution spot together give it an effective resolution north of 600 megapixels. The auto-exposure is also pretty bad ass.
>Not only are our eyes far more versatile than the most sophisticated camera equipment,
This is inaccurate. You do need a fairly good camera to beat the human eye, but only a couple hundred dollars. Cameras not only beat the human eye in total resolution and color depth, they beat it in peak resolution over an area much, much larger.
One of the main things you hear about cameras is that they have inferior dynamic range, but that is also untrue. A camera with an actual aperture (like the eye has the iris) is far superior. Cinema cameras are more than an order of magnitude better than the human eye, but even small cameras can be as good or better than eyes.
Reports that the human eye can detect single photons are highly exaggerated. Technically true, but on a very different basis than a camera. Compared fairly the camera will come out ahead.
The biggest "real" difference between cameras and eyes is field of view. Personally I have a 220 degree field. Although technically with two circular fisheye lenses you can get almost to 360 degrees, even individual eyes can have fields of view greater than 180. We know how it works and could reproduce it, but it's still very impressive and I don't think normal lenses do it.
Even a cheap webcam has higher "fps" and less blur than a human eye. Cameras are also much better at estimating velocity even besides that.
Some people are more sensitive to colors- especially infrared, and a number of people are tetrochromats. Cameras can naturally see infrared far deeper than humans although tetrachromatic/hyperspectral cameras aren't exactly common.
>we constantly maintain a mental model of the world that allows us to focus only on the things that matter, and accurately infer state.
This is not an advantage. What this means is that we aren't paying attention elsewhere. This is a very coarse first order optimization- cut out a ton of data, only pay attention to what you're paying attention to. It's tautological and it means you don't get to apply optimization algorithmically. This is a huge advantage for computers.
- I am very skeptical that even a cinema camera can exceed the dynamic range of the human eye in one exposure. It is probably more important that features never saturate or go black when the image is properly exposed than that exposures are possible in a wider range of conditions. Also, cinema cameras are as big as toaster ovens and cost kilodollars.
- You're saying the peak angular resolution of a camera with a near 180 degree field of view exceeds the angular res of clear human foveal vision?
- Dark sensitivity: Yes, you can detect more a lot more light if you make the aperture 200x the size of a pupil. Could a space-efficient camera compete?
- Fairly saying there is higher fps AND less blur in a camera means you have to show me a camera the size of a human eye, with the dynamic range of a human eye, with the resolution of a human eye, and the low light sensitivity of a human eye that can produce more than about 60 fps. You can certainly have more fps, but often at the cost of size or resolution or light sensitivity. You can certainly have less blur, but then that means you need a bigger aperture to let more light in so you can crank your shutter down. Are you saying a small camera can beat the eye on all counts?
So yes, if we stud a car with RED weapon 8ks, we'll probably exceed human perception. Can the same be done cheaply and hidden in the body of a car with current technology?
The human eye has a dynamic range of 10-14 f-stops[1], a Nikon D810 has 14.8.
The human eye has a ~10 megapixel resolution over a 20 degree view. Over 60 degrees that ~52 megapixels. There are DSLRs that exceed that. I don't mean cameras have the peak resolution over 180 degrees, just more than 20 degrees.
Cameras are certainly comparable eyes for space efficiency. Take away the frame, battery, electronics, simplify the lensing, and you have a very small device indeed. It would be better if we had spherical sensors, of course. The human eye's sensitivity can get up to ~1000 ISO after a long adjustment period. Cameras are much better.
>Fairly saying there is higher fps AND less blur in a camera means you have to show me a camera the size of a human eye, with the dynamic range of a human eye, with the resolution of a human eye, and the low light sensitivity of a human eye that can produce more than about 60 fps. You can certainly have more fps, but often at the cost of size or resolution or light sensitivity. You can certainly have less blur, but then that means you need a bigger aperture to let more light in so you can crank your shutter down. Are you saying a small camera can beat the eye on all counts?
Do cars not beat humans because they are bigger than people? There is no need for a camera the size and performance of the eye. Machine vision has no need of that resolution or dynamic range, and photography/film have no need of the size. Why would anyone make a smartphone with 50 megapixels? That's impractically large.
Even still, take the google pixel. By volume it's probably less than a tenth the size of the human eye. It's got comparable resolution (>13 MP), though lower peak resolution. The dynamic range is at the low end of a human eye despite the massive size difference. The FPS and ISO are better. All it needs is a fisheye lens. If a camera company wanted to exceed the human eye, they could certainly do it.
Human visual hardware isn't very good. Slow integration time, very small (~10 degree) high-resolution area with mediocre resolving power (~1moa). Good dynamic range, but more than is really necessary for driving. A few tricks like ocular microtremors.
Everything else you said is a factor of software, not hardware.
Perhaps don't be so condescending unless you actually have a really good point. You don't need to know much about biology to point out that humans get by without very fancy sensor hardware.
OK, but we developed or mental model of the world through those two cameras. I agree that we still have aways to go, the fact is only two cameras and processing is all that is needed. But we can do better with more sensors
If a company's solution to self driving cars with just two cameras requires developing a machine learning "model of the world" (I don't think it does, but it does make it a much harder research problem), then they are going to be years behind everyone else in shipping a self-driving car.
If a company's solution is able to maintain a real-time model of the world on top of which reasoning and reaction at human-level speeds is possible, never mind the driving cars - that's priceless!
That's the understatement of the week right there. We've been working on that tiny little problem of "...and processing" for about a century (wall time), yet the result is still quite rudimentary.
I think there's an important difference in requirements for engineers: an artificial system shouldn't need years and years of constant training. Sure, we make do with two eyes and ears but if I need to check my blind spot, I still need to turn my head, taking my eyes off where I'm going.
The movement, focus and light aperture mechanisms of the human eye technologically outstrip this description so ridiculously that I have to assume you're joking. Try designing and building a system that can change heading and focus as rapidly as the human eye, while handling nearly as high a input dynamic lighting range. Not even considering the physical space efficiency and impossibly low power consumption of the human eye relative to to do its job compared to any manmade sensors.
AI discussions often get caught up purely in processing and learning, but the mechanical and sensory systems of humans are also non-trivial to integrate into machines, and not to be underestimated.
>The movement, focus and light aperture mechanisms of the human eye technologically outstrip this description so ridiculously that I have to assume you're joking. Try designing and building a system that can change heading and focus as rapidly as the human eye, while handling nearly as high a input dynamic lighting range. Not even considering the physical space efficiency and impossibly low power consumption of the human eye relative to to do its job compared to any manmade sensors.
and I'm sure the majority of what you described is completely unnecessary for the purposes of self driving cars. I hope you enjoyed the few moments of pointless outrage you had when writing your comment.
It's really a question of timeframe. You are completely right that we theoretically only need two cameras. But by limiting yourself to 2 cameras (or cameras + other non-lidar sensors) you're just making the problem a lot harder for yourself. That's a lot more computer vision tasks your research team is going to have to solve, a lot more models to develop, a lot more failure cases (less redundancy).
Some of these vision problems have uncertainty over how long it will take to perform at human-level accuracy. Whereas if you go with lidar, I don't think there's any doubt the costs will go down. IMO on the market, a $5k sensor cost is also nothing if your car is actually fully self-driving.
The more I think about it, the more I am convinced that we humans are _un_suited for driving safely, given the equipment we have. Because we have only "two stereoscopic cameras on a swivel," we have to rely on a complicated system of mirrors -- often at least four (rear-view, two side-views, and a small blind spot checker) -- to get a full view of our surroundings, and we have to constantly swivel our eyes and neck to flip between front view, mirrored views, and instrument panels. We are unable to monitor all of those things at once.
Moreover, we have only an extremely crude way of communicating with other drivers to coordinate our actions. We have two turns signals, a brake signal, hazard lights, and a horn, none of which is guaranteed to be used appropriately. A lot of traffic jams and crashes could be avoided if our vehicles could broadcast their intended actions to each other in greater detail and with more reliability.
Crash data certainly bears out the fact that humans are not well suited for driving. While you say "We already know that you can be successful at driving a car in various weather conditions," I would instead say that in the aggregate, we do better than one would think, given our constraints, at avoiding crashes -- but we humans crash a lot. Millions of times a year in the U.S., killing tens of thousands, and resulting in some sort of injury in about half of cases.
So, whatever technology is being considered for self-driving cars, comparing it with the "technology" of human drivers is a natural baseline, but I would think that the greatest gains would come from breaking away from the equipment limitations humans have.
> comparing it with the "technology" of human drivers is a natural baseline,
I don't think that should be the baseline. You can have driver assistance like collision avoidance with human drivers. And lane correction too. Humans can be helped to drive safer without giving away complete control away to machines. That should be the baseline.
why does everyone here think I was suggesting that we should limit self driving cars to human capabilities? My comment was simply a comment on lidar v cameras. Obviously the cameras should cover 360 degrees around the car and have whatever additional sensors necessary for safety. Which would all be cheaper than lidar.
Limited sonar? I'm not sure what sonar has to do with driving. Between luxury cars with soundproof boxing, or constant road/wind house in a convertible I don't think sound plays any type of role in driving
I didn't read the comment you're responding to, but auditory input is an important part of a drivers toolset. I often hear an ambulance before I see it, sound helps diagnose if I have a flat tire or whenI need to change gears. Heck, car horns communicate information only through sound.
>“Once you solve cameras for vision, autonomy is solved; if you don’t solve vision, it’s not solved … You can absolutely be superhuman with just cameras.”
though I can see the argument for using a bunch of other sensors as well especially given that current self driving AI is a lot less smart than humans.
I wasn't suggesting limiting it two cameras but cameras should be more than enough to solve self driving. You don't need lidar which costs thousands of dollars.
You are right. Lidar is the right answer _right now_, because alternative requires too much computation. SLAM, Visual Odometry, Structure from Motion, Multiple View Stereo, PMVS, CMVS, or even raw NNs will replace Lidar when computation gets cheap and compact enough.
Even that isn't sure. Certification is a huge barrier, and simple systems win there. And once lidar has proven itself and gained enough miles, it would be hard to change, just for cost savings.
Will a bunch of self driving cars all using LIDAR interfere with each other?
Busy intersections could have strategically placed high resolution LIDAR "base stations" that wirelessly transmit the model to cars as they near the intersection.
You could avoid interference using techniques similar to ones used in direct sequence spread spectrum - rather than a continuous beam, send out pulses in a pseudorandom sequence, and look for that sequence in the reflected returns. With each transmitter keyed differently you can ignore reflections from other transmitters.
That sounds like a centralized approach to the problem. Which ive thought about, too. If the government provided more infrastructure to assist driverless cars it would probably accelerate adoption and the installation and maintenance costs of LIDAR turrets and other infrastructure would ideally be offset by the number of lives saved. Im also wondering if a something similar to air traffic control could be applied to self driving cars.
If you really want to decentralize, take down all the "DON'T WALK" signals, and let pedestrians use a smartphone app to tell them when to cross the road.
I think the more important goal than being completely autonomous is killing and maiming as few people as possible. Cars depend on a lot of immobile "base stations" like gas and charging stations, traffic lights, drive through fast food restaurants, and the roads themselves. Until cars can refine their own petroleum or generate their own electricity, replicate their own fast food, and pave their own roads [1], they're never going to be purely autonomous.
My question is how much do a bunch of cars an intersection all using LIDAR at the same time interfere with each other? Would it make sense for them to wirelessly share their models, and share an even better model made possible by high resolution LIDAR placed on poles like street lights? Then approaching cars could see around corners, and know about otherwise invisible oncoming traffic.
I'm not suggesting that every intersection and long stretch of road through the desert should be festooned with LIDAR base stations. But how about starting with every busy intersection on Market street, for example?
Wait you don't want your car to send all the inputs to Amazon or Googlefor processing in their data center, and just her responses back? "We see you are driving by a McDonald's. Your route has been updated to include a burger and fries!"
Reflections. How many self driving cars can see these? Every day that is what I have to look for when leaving a garage - the reflections, or shadows, of people and pets, as I come out of a parking garage.
The edge cases for self driving cars are massive - unless we dictate our roads and street to be machine friendly.
Pretty sure this is anthropomorphizing too much. Why look for the reflection when you can literally detect around the corner? More, if the system is fully automatic, just regulate the speed so that it can stop in time for events like these.
I don't know the exact state of recognition used in the state-of-the-art self-driving cars, but I wouldn't you be able to handle reflections if you use normal camera image recognition combined with LADAR? From a simple image, you can infer the distance to an observed object by looking at the size of its bounding box, and compare that to what the size of a bounding box of that type of object is at some known distance. After that, you can validate this distance using LADAR. If there are large discrepancies, you can either infer that it is a reflection, or you can maybe have trained a system specifically to classify if something is a reflection or not. That system would probably look at how much distortion there is on an object, compared to the rest of the scene, which could be caused by e.g. a non-flat reflective surface.
Like I said this is just some thinking out loud on my part, and I am by no means an expert. Does anyone that are more knowledgeable about this topic know if this strategy could be feasable?
I would assume that you would have this data about every object that the system can recognize. It would just be trained like any other system would be trained, because you can use the ladar for finding out the real distance. It, of course, will be difficult to guess the distance on objects the main system haven't been trained for.
The interesting part is that distance is not really needed, only good estimate of presence or absence of an object and a real easy model of how a mirror works.
Then you're back to "completely autonomous until OH WAIT CRAP WHAT NOW HUMAN TAKE THE WHEEL [crashes into a white truck across the highway]". Useful? Perhaps. Autonomous? Nope.
This video convinced me that self-driving cars are definitely going to beat humans at safety. Freaky how early the thing gets that there's going to be a crash and the distance at which it responds:
From the comments: "It has been brought to my attention, that some of these clips may be fake. A new video has been made that eliminates the fake clips https://www.youtube.com/watch?v=rphN3R6KKyU ".
Not to detract from the fact that autonomous driving has the potential to be safer than humans (if only because it doesn't get distracted), but none of these autonomous responses are "freakishly early". I've seen attentive human drivers respond with similar or better performance. As a matter of fact, let's examine how human drivers responded to one of the collisions in this video.
At 00:28.828, a human could tell that an accident is imminent -- the oncoming car will violate the median should it not apply immediate corrective action. However, other drivers are not obligated to wait until the aggressor vehicle crosses the median to take evasive action! Indeed, the silver vehicle (ahead of the dashcam'd vehicle) begins decisive and correct evasive action at 00:28.328. That vehicle maintains the same steering angle and at 00:29.295 crosses the solid white shoulder/"fog" line -- it's unambiguously an evasive action and absolutely not a normal lane change.
The autonomous vehicle only begins to brake/turn at 00:30.130 (the first frame where the car begins to pitch forwards due to the weight transfer). The autonomous vehicle has begun to take evasive action a full 1.8 seconds later than the silver non-autonomous vehicle. Awkwardly, the Tesla's reaction seems to be triggered not by the median-violating vehicle but by the white vehicle (who is attempting to take evasive action) cutting it off. Worse, the silver vehicle is already crossing the shoulder line a full second before the Tesla begins to brake/turn. This isn't even commensurate with human performance -- the two human drivers visible in the dashcam video react significantly earlier than the autonomous vehicle.
It's absolutely possible to make vehicles that respond quicker and more effectively to impending collisions than humans do. Sensing with radar and IR can give autonomous vehicles information that human eyes simply can't detect -- IR is useful for detecting human presence, for example, and radar can be used to detect positions of cars beyond-line-of-sight. Better sensing translates to earlier warning. Also, when the computer has full control authority over individual wheel torques there's a whole new range of operating regimes that get opened up -- and autonomous vehicles that exploit those have the potential to make cars much safer (see research like http://news.stanford.edu/2015/10/20/marty-autonomous-delorea...). This sort of tactic can use the tyre grip to the absolute limit can let the computers craft the optimal (given constraints such as the vehicle's position, surrounding vehicles/barriers, and tyre/road conditions) trajectory out of a collision and execute it with precise per-wheel torque control (via the ABS/ESC system) and high-speed steering input. Even the best drift drivers don't have that level of control authority over their cars!
There's lots of room to make autonomous vehicles safer than human drivers but this video is a demonstration that autonomous vehicles can be a lot worse than human drivers.
The scene at 0:18 also doesn't show a really good behavior. Normally you should get to full stop on the same lane as a vehicle in front of you if this one breaks hard. The car in the video has to evade into the other lane, which is not always possible and dangerous. The fact that it has to evade into the next lane means it either drove way too fast, kept less than required safety distance or did not recognize the front car breaking fast enough.
If you've got a camera facing backwards looking for cars in the other lane than evading into that lane might be safer in many cases where you can't count on the car behind you stopping in time. That's only a good plan for computers with sensors looking in every direction at once, nothing you or I should ever try when we're driving on the highway.
And yes, the car was too close. But the automatic systems don't kick in until they're necessary to avoid an imminent collision. Hopefully autonomous cars won't make that mistake.
> nothing you or I should ever try when we're driving on the highway.
I've done exactly that on highways and on non-controlled-access roads. It's not hard to keep a mental model of nearby vehicles (look at mirrors, if you see a car disappear from them but not go in front of you they're in some blind spot. When I know I've been tip-top at keeping watch like that, I've done evasive manoeuvres into other lanes just based on that. If I've been shoddy with my situational awareness, I do a quick head turn while doing the swerving and am ready to bring myself back into my lane should there be a vehicle in the spot I want to occupy.
Part of defensive driving is being proficient at swerving; as sharp decisive steering input can get you out of collisions that would be unavoidable via braking. Being able to swerve as fast as possible without losing control, on an open-loop basis (without needing feedback by looking at the road) and recovering is a useful skill to be confident in. I've practised swerving and thus can controllably and swiftly shift my car sideways and realign with my previous direction of travel -- it's not something I need to think about, it's as ingrained in my muscle memory as operating the pedals or the gearbox!
A few weeks ago, I used that skill to avoid hitting a pedestrian who had ran onto the road (without warning) from behind a parked car on a rainy night when I was driving at 35mph. Given my reaction time and the time needed to take the foot off the throttle pedal and onto the brake, I'd still have hit them at ~25mph even with maximum braking effort -- there wasn't enough distance. Indeed, braking would have given them more time to get in front of me, as they didn't seem to be slowing down / stopping. "Possible hit at 35mph" vs "more probable hit at 25 mph" is one horrid dilemma!
Given my (legal, albeit not so prudent) speed and how little warning I had (it was night and my driver's side window was covered in rain), when I first saw the pedestrian (through my windshield!), my immediate reaction was to swerve and not to brake. My steering input added a meter or so of space between my vehicle and that pedestrian. I did not hit anyone that night.
Self-driving vehicles should be capable of that sort of performance as well! Braking is not always the best option.
Many humans also won't be able to distinguish between inanimate objects and babies/animals on the road. When there isn't sufficient data, there isn't sufficient data. Machine learning is not magic.
That problem is hard, but because of cultural norms not as hard as it seems at first sight.
You rarely will find that baby without a stroller next to it, and without a human close by hurrying towards it.
So, if you avoid hitting that stroller and that human, and, next, avoid almost hitting that human, you probably are safe. A jury wouldn't easily convict you for accidentally hitting a baby that fell out of a stroller on the middle of the road and then was left alone without any adult supervision.
I would think the difference between a solid object such and a piece of concrete or steel girder and various low-density objects (plastic bags, empty carton boxes) is harder.
On the other hand, I guess it might be easier than laymen would think to use machine learning to build a "what's the density of the object on this photo" program that is in par with humans.
I wouldn't worry much. I have a few "fans" that follow my comments regularly and mass down-vote me. I also realized lately that my own upvote/downvote does not get accounted for.
Better than a human driver can detect a cat in the road.
In fact a failure where it slows until it confirms with another sensor (probably optical) is ok. Many a driver won't even see the object and will drive though it.
LIDAR is quite interesting. If there's anyone who has more information about automotive lidar, I'd love to chat with them, my email is visible on my tag :)
It'll be interesting to see how SDC makers handle the computational complexity of deep nets operating on these massive point clouds generated from LIDAR. It seems like these aren't getting any easier with Luminar claiming ~10 million points. Running that through a hefty 3D ConvNet could easily soak up a petaflop...
To my limited knowledge, they don't. Deep nets are mostly used by the self-driving companies for processing camera data for image detection and segmentation tasks like detecting other vehicles, recognizing street signs, etc. Presumably, having 3D point clouds lets you hand-code solutions to a lot of these tasks without having to use ML.
For as much as Americans complain about regulations, there's very little standardization in automobile driving environment (rules of the road, road paint and signage) or in the driver (uncle Joe can teach you how to drive in a few weeks, and essentially once licensed you're licensed for life as long as you pay a fee and don't become legally blind). Anyone who drives in the U.S. and either is a pilot or has driven in another industrialized country knows how non-standard automobile driving is in the U.S. This might make us fairly defensive and adaptive drivers, because our fellow drivers are so unpredictable due to non-standardization.
I think that makes the task of integrating autonomous cars a difficult task, not for the human driver, but for the computer. It has to be more adaptive and responsive with a lower error rate than the best American driver, while simultaneously sharing the exact same physical environment with non-deterministic actors.
Once we solve the pollution problem, all the HOV lanes can become autonomous car lanes. Only qualified cars use those lanes and they must be in autonomous mode. And conversely in mixed zones they can only be in some kind of hybrid mode. How insurance sorts out liability in the hybrid case will be interesting.
For all the non-regulation of US roadspace, I don't see any significantly different driving in Europe: having driven enough on both continents, it feels the same w/r/t unpredictable drivers. IMHO it doesn't matter whether the unpredictability comes from "has no training" or "has training but ignores it".
LIDAR is supposed to have low attenuation in water, however it will still suffer refraction. Hopefully it will function about as well as a human. The vision processor will have to understand that it's raining and correct for refraction where possible.
Snow is a different challenge, but a sufficiently smart system should be able to composite frames and appropriately fill in the holes created by being obscured by snow. It won't be trivial, but it should all be doable.
It sounds to me like they are discounting the possibility that an entirely camera based solution could be developed that bypassed the whole lidar cost issue altogether.
Perhaps because machines can't be held responsible for deaths they've caused. Though labelled 'autonomous' theirs is a somewhat lesser form of autonomy than you or I possess.
Coded pulses: rather than emitting a uniform beam, they emit a pulse and listen for that back. You can think of it a little like a bar code, a sequence of short and long, on and off. Then select for that on receive.
LIDAR units could cycle through randomly-generated patterns for each pulse - if they can be made unpredictable then it would be impossible for someone to confuse a LIDAR unit by copying its pulses. Assuming it's a time-of-flight LIDAR then the time required for an attacker to read a pulse and send back their own signal would be greater than the time of the original signal to return - and if the sending LIDAR has a sufficiently short timeout then even an attacker using Vanta-black to absorb all of the victim's lasers still won't be able to respond quickly enough.
Much the same way two routers broadcasting different WiFi networks in the same space and received by two different computers don't really interfere with each other. It's all just EM stuff, you can apply standard EM communication protocols like modulation. However standards will still be necessary, it can indeed be messed with.
Haha. Not much, except clobbering each other. This is not too probable with two routers, but with a dozen, all trying to work at 2412 MHz ("channel 1"), the SNR tends towards 0.
Nobody likes to talk about it, but they don't. It turns out there are some clever tricks you can do if you control all the cars, but if not you have to get competitors to agree to some protocols.
There are a couple difficulties with this. For starters, in order to make lidar eye safe, it has to use incredibly short pulses of light. You're saying we need to take that single pulse and replace it with enough of them that it has a unique fingerprint - realistically it will have at least 5x the energy. Add in signal degradation and a safety buffer and you likely need at least 10x the energy to uniquely identify sensors. Suddenly the invisible lidar is visible and dangerous for regular use. Of course this only applies to the visible spectrum - Luminar is actually unique in using a 1500nm laser. In addition to the above, it just makes everything a lot more complicated and expensive when everybody is racing to make it cheaper.
Lidars are highly directional and have low duty cycles so even when large numbers are present the amount of interference is small. So you do have to model a certain amount of noise in your lidar returns but you have to do that anyways due to things like sun reflecting off of shiny surfaces. Noisy sensors have been a fact in autonomous robots for as long as there have been autonomous robots and there are a variety of standard techniques for coping.
The thing is I'm often looking at other drivers and when driving pedestrians look at me. You can send them across a crosswalk you are stopped at in a car with a wave or a tilt of your head. You can tell that person isn't going to cross the street and is waiting for a ride because they are looking at their phone and then cars. Sometimes pedestrians wave me though when I'm in my car. I had a situation where an adult waved me across at an intersection but her kids kept going. I stopped, kids stopped after mom told them to and we were back to square one of our intersection dance. Things that sensors are going to have a hard time doing.
I think we'll get driver assisted as sensors can look 360 and further ahead. highways and less dense areas seem ideal for self driving.