Lidar is AMAZING for giving press demos on sunny days. For the real world with rain, snow, leaves, plastic bags, etc? Useless.
The future is radar + cameras + a LOT of software blood, sweat and tears.
Darkness is to eyesight as inclement weather/obstacles is to LIDAR
I am not sure what software does with noise from a lidar sensor but I have seen data from other noisy sensors and they are often useless.
In darkness it helps if you can hear the vehicles coming close.
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.
EDIT: Found this recent article...
https://www.technologyreview.com/s/604272/a-sense-of-hearing...
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.
Studies have been highly mixed: http://www.lifeprint.com/asl101/topics/deaf-drivers.htm
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.
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.
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.
Now imagine walking on top of a sky scraper in pitch darkness. Yes your eyes work in light, but in this case you will likely fall to death.
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.
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.
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.
But I think first we'll see cars utilizing tech as described in this paper:
https://arxiv.org/abs/1604.07316
...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.
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.
... all of which Waymo's solution also has, in addition to LIDAR.
Until then its just an attempt to make something that breaks at the next unanticipated exception.
Just to get our car out of the garage, I had to plead and negotiate with N vegetable vendors with makeshift stores on the road.
Also: bicycles, motorbikes, rickshaws(in human pulled, CNG and electric varieties!)and pedestrians mixed in traffic everywhere.
This is a very practical test case for a car on a road. Not just in India but anywhere in the world.
Instead of N vegetable vendors you could have N traffic cops. How do you manage the human interaction part in the self driving car?
Not to take away from anyone's work in this area, but I have no idea how long it'll take to go from "works in America" to "works in India". In many countries the safest option (to evade disaster) can occasionally be "floor it and break the speed limit" to get away from x dangerous thing. I'm not sure if that's something that Google is willing to write into an AI.
bike < car < van < truck
which makes sense because if you're the one who's going to come off worse in an impact then you really want to give way - especially if you have a massive painted tipper truck hurtling towards you!
It will be very interesting to see how self driving systems can cope with these local unwritten bylaws.
This is why self driving AI will require Hard AI.
India is a perfect test bed for these people to test their algorithms. And for heaven's sake why would you test it in some place like the US. Cars in US are pretty much trains on road any way.
But even in those cases it makes sense to test in India. Why? Sooner or later you will have some situation in the US which may resemble daily traffic conditions in India. Imagine a law and order situation where people are running around without regards to traffic laws. Or some other situation where traffic is being rerouted through a wrong way, In US may be as an exception traffic is being routed through the left lane(being a right lane drive country) etc etc.
For all these situations you will very soon need a test environment that provides you with all situations to test.
Which won't be India.
Because you obviously don't want to be testing unprecedented conditions with live subjects in actual traffic.
If anything, that will only happen after tons of simulations of such conditions in fake environments.
That doesn't make much sense, because the main (and most common) question would still be between vehicles of the same class: car vs car, and this doesn't solve it.
It's not going to rain or snow today, and if it would, then I can take the wheel myself.
I thought I remembered Nvidia presenting some additional stuff about it in their Tech demo recently.