Our eyes are not LIDAR, though, and we can drive pretty well.
In fact, the reason we have crashes is NOT our eyes’ lack of distance detection through laser return timing — having two eyes is enough for distance appreciation. We have crashes because of attention deficit instead.
At this point, there is no reason to believe that a machine can't achieve and outperform a human on a driving task given the same inputs. Sure, human eyes have 5 million cone cells and 1080p feeds only have 2 million pixels, but 4K has 9 million, and more importantly, that level of precision is unnecessary for regular driving.
And Tesla doesn’t even bet just on the visible spectrum; it also relies on radar.
The trick is in our wetware. What the brain does with visual input is not just trivial object recognition. It relies on a complex internal model of the world to both augment the object recognition and to sometimes discard the visual data as invalid.
So sure, theoretically cameras would be enough. But we're not yet there with software, we can't use the camera input well enough. So if you can side-step the need for not-yet-invented ML methods by simply adding a LIDAR to a sensor suite, then it's an obvious way to go.
Compare with powered flight: we didn't get very far by trying to copy the way birds do it. The trick is in the super-light materials birds are made of, and the energy efficiency of their organisms. We only succeeded at powered flight when we brute-forced it by strapping a gasoline engine onto a bunch of wooden planks.
> It relies on a complex internal model of the world to both augment the object recognition and to sometimes discard the visual data as invalid.
That in particular is what makes the hiring fascinating. This problem is Andrej Karpathy’s expertise[0]. His CNN/RNN designs have reached comfortable results, in particular showcasing the ability to identify elements of a source image, and the relationship between different parts of the image.
The speed at which those techniques improve is also stunning. I didn’t expect CNNs to solve Go and image captioning so fast, but here we are!
I think the principles are already there; a few tweaks and a careful design is all it takes to beat the average driver.
I think LIDAR will have a place, if they can get the per-sensor cost down to something reasonable (under $500.00 per sensor, for 3D 180 degree with at least 32 vertical res beams - or the equivalent).
But I think first we'll see cars utilizing tech as described in this paper:
...and variations of it to handle other modeling and vision tasks.
Self-driving vehicle systems are amazing complex; it won't ultimately be any single system or sensor, or piece of software or algorithm that solves the problem - it's going to be a complex mesh of all of them working in concert.
And even then, there will be mistakes, injuries, and deaths unfortunately.
Actually in the dark you often drive as a leap of faith in the state of the road. I.e. with very little visibility on what can come from the side of the road (no light) or after a turn. We shouldn't. But we do.
Yes, but it will cause you to slow down. Or at least, it should. As soon as you have less than your stopping distance of space in front of the car you are going too fast.
In fact even in daylight you drive a lot as a leap of faith. When the traffic light is green for you at a crossing and you see a car arriving on the side, you assume that you have the priority, that the car will stop and you go ahead without adjusting your speed to the coming car. This is a leap of faith in the fact that all other cars will follow the rules.
There's always the option to enable the headlights in such a case. LEDs switch on way faster than incandescent bulbs. And if they're needed at all - a camera has way more flexibility in brightness input range than a human eye.
A car like a Tesla has also highest-quality maps and GPS sensors - these alone are way better than what you get in your smartphone and are enough to keep the car from going over the cliff.
It's pretty obvious that a self-driving camera-based software would take advantage of headlights on a car, just like a human does? So it never has to drive in full darkness.
What's the point of that analogy? Darkness can be fixed with lights on the vehicle itself. Inclement weather is entirely outside the control of the vehicle.
Bingo. When we drive a vehicle, we use so much more than just our eyes to sense the environment, and hearing plays a very large part.
I believe that it is something that warrants research for self-driving vehicle usage; I don't know if anyone has done such research, but I haven't seen any papers on it yet. If not, it seems like an underappreciated sensor aspect that could potentially greatly augment self-driving vehicle capabilities, and would be a very simple and cheap sensor to add to a vehicle as well.
While it seems to be focused mainly on diagnosing issues with vehicles before they become larger problems, there are hints about it being used for self-driving tasks as well.
I would be comfortable saying that the advantages a deaf, many eyed, always alert self driving system would far outweigh the safety of a hearing, two eyed and sometimes alert human driver.
Not really. Self driving cars should be able to drive in all of those conditions (and one condition might even quickly turn into another, e.g. sunny into rain).
If a technology only helps with some of the cases (e.g. fair weather) and does not work for the others, then there are two cases:
(a) A single replacement technology will be found that works in 100% of cases.
or:
(b) The technology will only be used on the cases it works well, and the other cases will be handled by some alternative technology equally only suited to them.
In the case of (a), Lidar is indeed useless (or at best, only used as a supplementary technology in favourable conditions).
And I fail to see how (b) can be the case -- that is, how there can be another technology that will solve the rain/snow/night driving problem, but which cannot also outperform/replace Lidar for fair weather driving.
Isn't it interesting that we have five senses, when we could just have one that works in 100% of the cases? A third option is a system based on multi-sensory inputs. Several inputs that are just marginal on their own can provide good performance when combined.
