Tesla's self driving algorithm's overlay [video]
tesla.com
tesla.com
This new video is at n00b level compared to that.
[1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
So jitter/flicker can be cleaned up/smoothed out and the data can be massaged in ways that a real time system may not be able to do.
This is a presentation. I'd be /very/ surprised if this animation was from RAW data as-is.
Also there seems to be Lidar data (point clouds) which Tesla doesn't have.
So while this means bounding boxes may have less detail in Tesla's system this is not an issue as long as they are not smaller than the physical object.
Having worked in the automotive space in the last five years and seen lots of those I'd not say one is less impressive than the other.
I’m not sure what is implied with saying it’s a recording - both the Google and Tesla presentations are “recordings” and equal opportunity to pick best case examples, but I would bet strongly there is nothing not RAW = “real time“ for their respective compute platforms.
The top down viewpoint helps show off the quality (still by no means perfect) of the world representation. If you projected Tesla’s model into 3D you would see far more jitter than in the video overlay for a variety of reasons.
That said, I think comparing them directly on specific technical components is a bit of a sidebar. They are taking two very different paths along the way to a still ambiguous problem. Both are leading their respective approaches, but have fundamentally different and unproven assumptions.
Also worth looking not just at how accurately objects are detected but what the visualizations show about the intent of other road users. The Google video shows predicted trajectories for important objects in a number of scenes. We don’t get to see any of that clearly from Tesla, and that is by no means a small part of the problem. Not sure if it is there and not shown, just highlighting there is a lot more downstream even once you are finding objects reliably in the sensors.
So, not only can you afford a tesla vehicle, you can go out and buy one today.
Edit: wording.
[1] https://www.theverge.com/2017/1/8/14206084/google-waymo-self...
The car is aware where it is in the world, so a birds eye view is more representative of what the waymo car actually sees, it’s just one of the many ways the waymo car has better data to work with
The technology might be/seem superior, commercially Tesla has cleary won the race..
Although I wonder which is more truthful. I bet Google’s demo takes significantly more computing power, was cleaned up for the demo, etc
Turning point clouds into objects is child's play compared to plucking objects out of cameras.
The van had come to a complete stop with room to pass a USPS truck (it appeared safe to me from 25 feet away), and didn't begin moving again for a few seconds. She then took control.
Of course, this is annoying, and being so timid that you get rear ended is unsafe, but the Tesla approach would be the delivery truck is detected as a stationary object and ignored, so the vehicle would accelerate to the set speed in 3 seconds and slam into it. (See the many reports of Teslas running into stopped emergency vehicles). Of course, Tesla will tell you that their car isn't really self-driving with one side of their mouth, while telling you your car is equipped for self-driving out of the other side.
That reminds me of all the "AI power" we already use which is backed by low-wage contract workers who listen in.
My guess is the added sensors may get Waymo to market sooner, but if Tesla can commoditize this technology for far cheaper they will win in the end.
1.25 million die a year in road accidents. We should try to do better than human.
You're right they don't report disengagements, but they still maintain a autonomous test vehicle permit in the state, according to the DMV website. Very strange indeed. I think (2) is probably right.
For Reporting Year 2018, Tesla did not test any vehicles on public roads in California in autonomous mode or operate any autonomous vehicles, as defined by California law. As such, the Company did not experience any autonomous mode disengagements as part of the Autonomous Vehicle Tester Program in California.
https://electrek.co/2019/02/13/tesla-autonomous-mileage-cali...
Nothing about that changes due to semantics (your #1) or if they test elsewhere (your #2).
They both use visuals only and are easily confused when situations are a little different than 'normal'.
I even think Openpilot performed a little better than Autopilot but because Openpilot only has a forward looking camera it fails often on tight corners.
Google on the other hand is aware of the complete 3D surrounding. So even if road marks are gone or are unclear it still can estimate where the vehicle should be on the road.
When the car has no real idea of its position in 3D space this gives problems.
https://www.theatlantic.com/technology/archive/2017/08/insid...
This was a really really good read, especially into how Waymo simulations could generate data like seen in this video. It's from 2017 but still an extraordinary article for the quality of engineering insight.
Since they updated the neural net to also recognize the vehicle orientations the dancing has stopped.
I have seen a lane change cancel recently, a couple weeks ago though.
You can tell the car is a 'nervous' driver. It plays it way too safe, but I guess that's a good thing at this point.
You could probably model inertia with n prior frames of probability fields.
On the latest software and with HW 2.5, this is not true. It's still very much there.
What would be worrying is the model misclassifying an object, not detecting it at all, or having the bounding box consistently off.
Including human vision. The raw sensory data is pretty messy, and with some ingenious experiments some researchers can get a glimpse of exactly how messy.
The research on GANs also shows how computers can be fooled by things that wouldn't confuse humans.
in motion the car drives just fine with the caveat they have not enabled signal recognition. I use TACC and at times full AP on my daily commute which includes road speeds from 35 to 55. I particularly like it on rainy days. I treat it like having a high school kid being chauffeur... I am a back seat driver who just happens to be in the driver's seat.
as for visual representation like in the video or waymo's demo videos, like many other things in life when you see how the sausage is made it is a wonder how we all survive it. The key difference between Tesla and Waymo is Tesla is not geo fenced, same with Cadillac's supercruise which is not available except on interstate.
who has the best solution, I am not willing to place a bet on that yet
Basically, no, you don't just throw it at some Bayesian math or a Kalman filter to make it look prettier. Yikes.
But yeah, the scales are heavily tipped.
However the main difference is that, when we are consciously looking directly at something, we can almost always tell with 100% certainty what we're looking at, up to a considerable distance. I can see a car pulled over to the side of the highway a solid half mile ahead sometimes, and have plenty of time to respond. Computer vision doesn't have this additional strength.
As always though, the strength that computer vision has over us is it never gets tired or distracted, and it never operates in "default mode" where sensory inputs don't get full (or even much at all) conscious attention.
Even when some new information forcefully comes into play, your brain is often able to adjust your memory so you believe you knew it along, so long as the initial percept is fresh enough and had enough uncertainty.
All of this feels to you like a perfect unbroken stream of direct seeing but it is an illusion. You don’t see anything directly, you get fuzzy spurts of probability and turn it into your world in your mind. A world that’s likely to be unrecognizable to the next person.
That's just your brain again. You might mistake a bike for a lamp post, and switch between beliefs several times, before you figure it out, then convince yourself you knew it the whole time.
I bring that up because my hypothetical personal questions working on a system like this is 'smooth' decision making. Objects, lines, are jittery and falling in and out of recognition, but the actual control inputs to the car are smooth.
I know, good conditions and all that, but I always find it quite remarkable watching any sort of automaton make relatively fuzzy decisions. I'm very curious to know more about how this system 'thinks' about the things it 'sees'.
Imagine if you could see the raw input from an eye - it would be a big field of view, but mostly blurry, mostly not in full color, with a blind spot hole near the center, and the whole image jittering around violently.
One trait of practical real-world intelligence is ignoring 99.9% of everything. It usually works.
https://www.tesla.com/autopilotAI
Definitely someone's dream job ;) I particularly like the applicant query: "Tell us, what extraordinary work you have done?"
I wrote code that worked....
I like boring things.
Sometimes it's my own code.
One issue not discussed enough is all this push for automation really needs road marking guidelines pushed down from the Federal level. While the feds can hold domain over the interstate system or roads it can be maddening the differences on right of way rules to simple markings at state level
That would be a mistake. You don't take a safety critical system and rely on a nationwide beauracracy getting all the details right to make it safe, or even effective. We've had the discussion here before, and the only way for full level 5 autonomy is a general AI capable of most everything a human is. Until people realize that it will be an endless stream of "we just need to fix these corner cases" or put more constraints on the physical world because the cars aren't smart enough.
The interstates follow very strict and powerful standards that make us all so much safer.
https://en.wikipedia.org/wiki/American_Association_of_State_...
I can assure you, there are hundreds of times that "strange" things happened to the road. Odd or incorrect lane markings, no lane markings, lanes beginning from the right and left, lanes ending suddenly on the right and the left, lanes marked as merge that were not, lanes not marked as merge that were, etc. etc. etc.
Every time I thought I could just stay in my lane with cruise set I found I was severely mistaken.
Collectively decide what needs to be done for something to be considered safe enough, then regulate that it must be done that way for it to be allowed. Maybe this could be "self-driving car approved roads" and self-driving cars must check their navigation systems and only travel down approved roads. Drivers then get to lobby their local or state councils to make more roads follow the self-driving car markings and signage, and when they have, cars become permitted to drive down them next time they update.
Until people realize that it will be an endless stream of "we just need to fix these corner cases" or put more constraints on the physical world because the cars aren't smart enough.
That is one way of considering trains and trams, and they're good enough to be useful with extreme constraints on what they can do.
Who will fund that in a way that ensures a timely rollout (as in "sometime this century")? Who will pay to maintain it?
Unfortunately, self driving cars are just gonna have to deal with infrastructure as-built.
Yup especially because every country is USA.
Musk was bullshitting when he made that prediction. Maybe he did it because he had bought his own bullshit. Personally I think what's more likely is that he was cynically conning people.
In reality, I think no-one is anywhere close to fully self driving cars. I would be surprised if we saw fully self driving cars any time in the next 50 years.
The whole field of fully self driving cars has been astonishingly full of empty hype, for some reason often believed by otherwise quite smart people.
I don't understand why so many smart "tech people" fall for the hype.
Maybe it is because "machine learning" is just abstract enough that even the most jaded developer thinks they can treat it like a black box where if you pour enough videos, photos and LIDAR readings for training into the top it will somehow spit out a fully autonomous self driving car at the bottom.
I do find it funny though. On the one hand Alexa only manages to turn on the lights successfully 50% of the time yet somehow we will magically have self driving cars capable of safely navigating the roads any day now. I mean for fsck sake, we don't even have a thing that can wash and fold clothes automatically but somehow self driving cars will be on the market any day.
Like, if we cannot even get voice recognition to work right, how on earth will you tell this magical car which street parking spot to take in a busy city? How will you tell it to pull over to pick up a friend? Hell, how will you tell it to go through a drive-through at McDonalds? A touchscreen?
And that's just on the decision to not use LIDAR because "it's dumb", not the whole concept of self-driving as it has been marketed being decades away.
[1]: https://techcrunch.com/2019/09/16/waymos-robotaxi-pilot-surp...
[2]: https://www.theverge.com/2019/10/10/20907901/waymo-driverles...
There used to be, on slashdot and I believe in the early days of HN, this running complaint of the new-at-the-time CSI-style shows, specifically the "enhance" trope: "you can't reconstruct a license plate from a bad frame in a video. That information is just lost. It's not there anymore", they would say, usually with all the aura of letting you in on a secret only a very smart mind could gleam, although there were five others in the same thread making this point.
Today we have superresolution algorithms that can reconstruct license plates from low-res images. Turns out the information wasn't really lost, at least not in the sense applicable to the situation (i. e. you are allowed to train on other data).
Many tech people dismiss such progress as "just statistics", but I haven't seen much of an attempt to find a definition of intelligence that is meaningful different from "just statistics". In fact I doubt it's possible within the realm of science, i. e. without resorting to mind-body dualism.
As to driverless cars: they exist, right now. Google does thousands of miles without any need of human intervention. Yes, maybe it doesn't yet work well enough in a hailstorm. But predicting that these problems will endure for 50 years plus, against a combination of restricting these cars to certain situations, improving the models, and/or improving maps, seems at least as overconfident in your ability to make predictions as those made by self-driving optimists.
One thing that Elon did is gathering lots of video data instead of perfecting LIDAR, and at this point it seems that he was right: until detecting objects in 3D from video data is completely solved, self driving can't work. After it's solved, LIDAR is not needed.
- Tesla & SpaceX have a reputation for being a terrible place to work
When I'm looking for jobs, my main criteria is market cap / number of employees, and Tesla (as most startups/small companies) was very bad before the stock price went up. Now with high stock prices Tesla can afford to pay market rates (even if it's in stock).
All big companies grant stock based on monetary value at the time of the grant. If you join company X when their stock went up 2x, stock grant will be smaller 2x.
You can get market rate, from historically underperforming company, only if you got grants that appreciated, don't expect grants to improve pay rate.
You don't care about the work culture at all?
I really don't understand this thought. Every self-driving car company has more raw data than they can realistically use. I don't see how Tesla has some advantage
As for firing, well, who knows... The story about Elon's assistant was apparently rife with misinformation, as these things often are.
That is not what Elon predicted at Autonomy Day last April. He thought they would be feature-complete by the end of 2019, which is to say, that all the basic code paths necessary for best case city driving would be functional.
If you've done software development, you understand that getting to "feature-complete" is different from "code-complete", which is also different from "Beta", which is different from "GA". Those are each progressively later stages in the software development / release cycle.
Feature complete is merely the day that you can say that there is some functional code in place for all of the code paths that you expect to write. You would expect to be able to do an internal demo showing all the functionality working in the "happy case" at that point. Usually QA has not really even begun in earnest at this point. It is by no means the point where development is "done", which is for FSD in fact, approximately, never.
Code complete isn't always distinguished from feature complete, but in my experience, code complete would contemplate negative testing, error handling, and alternative processing modes which might not have been implemented at the point of "feature complete". Code complete typically signifies that from that point forward only bug fixes will be added into the next release.
Some projects can reasonable be expected to remain in testing, validation, and certification for several years after the point of "code complete".
To try to say the hardware for FSD is now present in 1M vehicles (which I doubt) is disingenuous to the insinuation Elon made that FSD is right around the corner. They're not meaningfully closer to FSD today than they were three years ago. Somewhat closer? Sure. A few feet on a journey that's a few miles though.
I do think anyone taking this has to expect grueling and challenging work. The job description practically demands that.
I worked for an erratic, sociopathic boss once. I quit after about six months. Unless you're a masochist or truly have no other options, you'll soon realize it's not worth whatever extra money or prestige the company name might possibly come with.
It was, but software often goes over-schedule, especially in a brand new field.
As it is we're on about Autonomous Driving Promise Mk IV from Musk, "fully autonomous, coast to coast, by the end of 2020". (That's after 2018 was missed. Which was after 2016 was missed.)
> specially SpaceX has been rated very highly in some rating I have seen.
IIRC, SpaceX has a COO who handles much of the day-to-day management, so the employees there are shielded from much of Musk's management style.
Love them or hate them its pretty cool Tesla put this video out at all. Certainly gives us all a lot to talk about.
Not that I ever had any doubt into how complicated real-time video analysis could be, this just makes my appreciation of the complexity of the problem that much more qualified.
In NYC, where I live now, the de facto rule seems to be if you hesitate at all then the other person just goes, rules be damned. In the rural south, where I'm from, there's a lot of "no no, you go first" waving/gesturing/light flashing, which I'm curious if/how a self driving car would handle. (Do we need to give the self driving cars hands to gesture with?!)
“A discrete decision based upon an input having a continuous range of values cannot be made within a bounded length of time.”
The paper is available from:
http://lamport.azurewebsites.net/pubs/buridan.pdf
It shows that under very general assumptions, a decision cannot be made in bounded time, leading to starvation (metaphorically and also literally). An example is a 4-way stop with other cars arriving at various times.
The paper's history is also very interesting:
It gets messy when people try to be polite or don't understand the rules correctly, but otherwise it's pretty easy.
It might depend on your jurisdiction, though.
In the late 70's in Connecticut, I was taught the first person to arrive goes first, followed by the person to their right and so on.
But there are so many holes with that heuristic itself! And putting aside guilt and anxieties of humans that prevent them from being assertive or committing--it's just a cluster fuck.*
However, I have noticed that people are becoming more comfortable with zipper merging in major cities on both coasts, so if we could arrive at some consistent 4-way stop rule that's 1) succinct, and 2) easy to remember, I have hope. :)
[* I'm referring to San Fran and the South Bay... but still better than my experiences driving through Italy and in Bengaluru!]
In practice we live in a world of (very illegal) rolling stops, people beckoning at each other to go first, people inching forward, etc.
That's not even considering some very stupidly designed four-way stops here in Illinois with two lanes on each side -- how the heck is one supposed to even figure these things out? It ends up being a game of chicken.
I should be able to broadcast somehow (using a voice message?) to nearby cars that "I'm going now, don't go".
You'd have to ask all kinds of wild questions too like:
"What if kids played a prank and re-painted road lanes on a highway at 50MPH?"
"What if a sand storm happened?"
"What would the car do if a tire blows out at 55MPH?"
"Could the car detect a Tsunami is coming and abandon a trip assignment?"
And that's just a tiny tiny sample of scenarios necessary.
Some AI elements may exist within the network, but I'm pretty sure they are only running on the back end in terms of maps. The computing power to really permit the car to run autonomously, and to "adapt and learn" about driving only comes with experience and it also takes a lot more computing power and storage than your home computer can handle, much less the components in Tesla cars.
Launching Self Driving Cars so close to their infancy is pretty reckless if you ask me. People are being paid off and buried online by paid staff online to keep quiet about all the accidents and issues with them.
To make self riding cars reliable at this point in time, they pretty much need perfect circumstances, and they need to operate on fixed, predictable, and well maintained paths... Kind of like a train. They should really work on this technology in mass transit, shipping, big industry and on planes before pushing out thousands of smaller vehicles with it.
There is plenty of proof that development teams aren't and may never be flawlessness enough to make error free updates, if you don't believe that, then just ask Boeing.
I need this feature, and I would prefer this to full autonomous driving.
A computer that warns you when you're about to make a mistake is achievable today and will increase safety for everyone.
I'm speculating but I think the reasons it veers to the right are some combination of the following: 1) There is water on the left hand side of the lane. I was going back and forth on the video and you can see "Raining" along the left edge turn green. So, it has detected the water and is avoiding it? At the same time it has turned green "Wet road". 2) There is a vehicle on the right side of the lane - it has been identified with an upside down green triangle / crosshairs and a square (is that block the license plate?) and the Tesla is "following" it.
Again speculating based on watching the display change.
I found this clip amazing for the following reasons: a) Around 0:02, it starts to notice the right edge of the road. Along the right edge of the video, you can see the estimated road. Around 0:04, you can see the right turn lane and the dots along the right edge change to show the vehicle has detected it. b) Detection of arrows on the road surface. They are labeled with "RA", "FA" and "LA". RA = Right Arrow, etc. c) Around 0:04, a "container" type of black outline appears on the right edge - it is a vehicle! d) Around 0:05, another container appears in the right turn area and it is another vehicle. e) Around 0:09, another container appears on the left middle and it is another vehicle.
Jumping to 0:22: f) It has seen the vehicle about to enter the scene from the left and "CutinExcited" probability starts to increase and when the vehicle has moved through the scene, it goes away. g) If you see the frame at time 28668.2612540 (?), it has picked up and classified a person on the left of the red 44.
Super impressive real-time classification.
"May potentially swerve into other lanes to avoid puddle"?
And yeah, it literally runs a stop sign. In their own marketing video.
And Summon has a warning that you need to "pay full attention to the vehicle".
Unless you look at their marketing, in which case it also tells you that you car will come "as you are being distracted by a fussy child".
50mph = 73 feet per second / 13 frames per second = 5 feet per frame. This still way faster than a human I'm guessing, but seems very slow.
As an aside, how do they get training data from their existing sold vehicles? over cell connection probably is too expensive I assume, so maybe via wifi?
https://www.researchgate.net/publication/233039156_Brake_Rea...
17x faster is an impressive improvement
There's a reason everyone else uses lidar...
Self driving cars is a case of doing the wrong thing better. I agree with the parent and there are better solutions out there.
A problem I think is the visual appearance of a tunnel changes as you move in relationship to in in a way that a static image would not.
I'm not behind any proxies - I'm at home, connecting from Brazil here.
Is the car seeing enough on its own that we as humans would be comfortable driving with just that information?
Orange labels:
O - Opposite traffic lane (used together with LA or S)
F - Same-direction traffic lane (used together with FA,LA,RA or S)
FA - Forward Allowed
LA - Left turn Allowed
RA - Right turn Allowed
S - Stop sign/line
C - pedestrian Crossing
T - Trash can =) (see 0:32)
Black labels:
P - Pedestrian (see 0:24)
M - Motorcycle (see 0:28)
C - regular Car ?
V - Vehicle or Van ?
K - truK ?
S - ?
L - ?
At the beginning, utility truck is labeled first as S, then as L, then finally as K.
Almost all cars are labeled as C, some are labeled as V - in my observation mostly Vans and SUVs.
In my experiments with the implementation in OpenCV I haven't gotten good results, especially with noisy detections of lines that aren't there. But here they seem to get good line detection without many false positives, despite difficult properties of the image such as worn down paint and low contrast between the paint and the pavement. Anyone know what they are doing that works so well?
I don't know for sure, but I imagine these days the techniques are closer to what you're more likely to call Machine Learning. Probably neural net classifiers trained on manually tagged data, and maybe augmented with a lot of map data. Maybe with some memory too -- I want to say you'd use a particle filter, but there's probably some newfangled ML technique that does a better job than those.
Exciting to see that slowly but surely we are starting to approach the future where a true AI will be born.
This doesn't really inspire too much confidence, IMO. It makes me think their FSD efforts are on par with their autopilot implementation.
Somehow I always expected they also used some sort of depth camera to detect distances instead of figuring it out based on pixels.
The difference being that all Teslas manufactured in the last two years or so have this camera sensor system installed on them, so they can all be upgraded to self driving with a software or computer upgrade. No one else is shipping cars that can be self driving. So Tesla has the advantage of collecting a huge data set of real world data now, and if they get the system working they can somewhat instantly “activate” a huge self driving fleet. Other companies have more expensive sensor systems such that the price is too high to sell the vehicles (only rent as taxis), and they will have to start manufacturing vehicles after they get everything finalized.
If Tesla and Waymo perfect their systems at the same time, Tesla will be way ahead.
Do Tesla owners know about this? Is this an opt-in feature?
I also think that a deeper understanding of the mechanisms and techniques required to reduce jitter might offer some insights into ways of handling adversarial images.
That was Uber, just so you know
At 22 seconds at the stop sign, after the first car drives past what if theres a car that breaks the stop sign and ends up ramming into the tesla (as a human sometimes you anticipate this by seeing that the car is not decelerating as it is approaching the stop sign)
Surely that can't be right
If you've seen other self driving cars, they usually have no trunk because the entire thing is server racks.
The reality of driving (as a human) is that most of it happens on autopilot anyway. It's rare to deal with an anomaly. So realistically, it makes total sense to keep refining these algorithms and make driving safer and more pleasant for everybody.
I just find it very hard to understand the perspective of those that are opposed to self driving cars.
On the other hand, there is a lot of hype and promises being made without the results to back them up.
I remember when credit cards, caller ID, online shopping, tax software, etc were all new. Each was loudly denounced as evil for various reasons; eventually the benefits became so obvious that we all now use those techs with little concern - even though many/most of the concerns were warranted. Just became too convenient to use them.
Self-driving will be the same. Scary concept to hurtle down the road completely & existentially subject to a machine built of (per A.C.Clarke) pure magic. Once in one, and seeing how smoothly & smartly it transports you, people will quickly embrace self-driving cars ... especially when they discover how too darned convenient it is to use them.
"Any sufficiently advanced technology is indistinguishable from magic." And self-driving neural-net cars, to most, are flat-out magic.
There's a big difference between seeing and interpreting. The car sees literally everything and can maybe identify most things, but a pedestrian window shopping across the street isn't really that interesting and neither is a bollard. As humans, we filter out the unimportant stuff to focus on the important stuff. We can also pickup on subtle clues in posture, through eye contact and even through the driving style of another person. The car will see all this, even in people across the street, but it won't be able to interpret it.
Humans are still way ahead of anything close to autonomy in most situations and it will take years for anyone to get close to covering 95% of situations. Removing the steering wheel needs way more than 95% covered.
Right. So the marginal benefit of a self-driving car to people who feel that they already drive on autopilot most of the time is low. Especially when they weigh that benefit against the risk of putting their life into the hands of a 4000lb machine controlled by what? A computational system programmed by the same group of people (in the general public's mind) that brought them clippy, the Hawaii nuclear missile scare, algorithmic flash crashes, bloated ad-filled websites, telephone bots, 8 remote controls per living room, and Flappy Bird.
(Truly) self-driving cars are, inversely, completely outside the control of the "driver". Anyone who has considered it even briefly has realized that governments will inevitably mandate lockout systems preventing people from using their own car if they are a wanted felon/drunk/a dissident. Such restrictions are completely impractical on traditional vehicles.
The vehicle misclassified Elaine Herzberg so many times in the seconds leading up to taking her out at 40 mph - its clear to me that none of this is ready for public use.
We are so normalised to vehicular violence. This would never be allowed in any other sector, say healthcare for instance.
[0] https://www.ntsb.gov/news/events/Pages/2019-HWY18MH010-BMG.a...
That should trickle down during time, but the fact that a self driving car is still a car won't. We need better and efficient trasport and self driving cars won't chage that very much.
I think few are “opposed” to self driving cars, but I’m not optimistic about when we’ll see the tech being widespread (level 5 self driving), and I think the ethical can of worms might delay it even further.
I’m optimistic about cars that drive very safely for 95% of the way on 95% of all trips, with equipment that isn’t very expensive
> if you look at this video and compare the amount of stuff the camera catches AND processes, with the things you are seeing & processing, it's really hard for me to see me being better at it than a computer.
Such a handwavy argument is worth exactly squat. What does this mean? Brains are hugely complicated machines which we barely understand in any reasonable detail. You see a handful of lines and text across the screen and it's sufficient to make you go ":o"? Enough to conclude it must be better than a brain?
So far I only see a world of promises and predictions of "self-driving cars are 5 years away", but no real tangible hard evidence that self-driving cars are better than humans. Point me to that, not to a fancy video, before we can have a discussion.
Southern California and the weather it experiences is a poor representation of what is required for self-driving to replace humans.
The real issue is decision pathways which you can’t depict in a video, and are the real issue with self driving. The sheer solution space is so massive that even simulating with current DL techniques isn’t feasible with current hardware.
Having freedom and control over a physical system that is bigger and more powerful than you, is a liberating experience for those whose spirit is used to a smaller, lighter frame.
Car manufacturers know this - they cater their interior design to appeal to this factor - and it is indeed a cultural phenomenon well and truly imprinted on Western society.
"Freedom means, freedom to drive yourself anywhere you want."
This 'liberation' has become a chain. Real human beings spend hours in these containers, driving themselves somewhere, feeling powerful.. driving to their work or home, or whatever.
2 to 4 hours a day, on the mobile throne.
When computers start to take that freedom, its going to tweak quite a few freak-outs. And really, why shouldn't it?
Ultimately, automotive industries are hedging their bets that cars will no longer be personal possessions, but rather something you summon on an as-needed basis.
This would be a highly desirable condition for the rent-makers/lease-share holders, who are really pushing this forward - along with their buddies in the insurance mega-industries, who stand to gain a great deal more control over their customers lives when it comes to computerised automation.
Myself, I'd personally prefer cars were user-serviceable and user-operable, under no conditions do I need a computer, just make me a better car. Preferably electric, simple as possible, and ships with a manual.
I wonder if the fear has more to do with it simply being different. The set of things that a CV system misses is likely quite different from the set of things that a human misses. A CV system is likely to make mistakes that a human would never make, and even if those mistakes are far fewer in number, they make it look bad to humans.
We've all seen electronic devices do this. When an Amazon Echo interprets the sound of a tea kettle as its wake word, we're left with no explanation, and no way to rationalize it other than to assume the device is, in some way, bad at its job. Surely a human would never make this mistake, we think, and so therefore the device must be dumber than a human at word detection. And yet, there surely exists a class of noises that a human would mistake for a word that an Echo would not - but that doesn't factor in.
Anyway, this means that a CV-controlled car would behave, compared to a human, strangely. When one's intuition about the car's behavior, tuned from years of experience both driving and observing cars, differs from the car's actual behavior, it becomes harder to make predictions about how the car will behave, and from here comes a fear and a sense of uneasiness. And when it makes a rare mistake that a human would never make, because it's perceptual system is entirely different, we assume its dumber than a human all-together.
By normal standards, it's insane to expect non-experts to maintain and operate heavy machinery, in public streets, half asleep or half paying attention.
It's also a pretty absurd way of providing mass transport in most places.
So if you're going to throw out normal risk assessment as a starting point, where do you go from there? People know how bad quality software can be, how ubiquitous bugs are, and how often programs are deep morasses of strange and perverse hacks, and how it only doesn't (normally) kill people because the stakes are small. Then Uber's car actually did kill someone, and predictably, the familiar problem of bad, buggy software looms larger, as a risk, than the dull and more or less commonly accepted fact of cars hitting people all the time.
Very common threats, like pneunomia, poverty, or cancer, tend to get depreciated in comparison to symbolic, unusal threats - and I think it's pretty unsurprising that on HN, people feel threatened by the possibility of bugs not only ruining their day, but actually running them over.
For example, if it were the case that safe driving cars were on average 10% safer in most situations.
Wouldn't there be pressure to reduce deaths and injuries by banning manually operated cars.
What do you think about self-driving cars being a source of data for private and govertment organizations on where you are at any time, or your travel patterns?
Do you think there would be a time when people who displeased the government could be essentially stranded because they can't get access to the self-driving network?
I never expected the level of survailance and scanning happening in UK/China, etc.
Are self-driving cars (possibly with biometric face and fingerprint scanners), another possible entry point into survailance?
I've been wondering about the privacy / control aspects. What do you think?
Once you observe how these things behave in severe weather, you get a little bit more perspective on why it simply can’t work. Not with today’s infrastructure, not with today’s technology, definitely not in mixed human / autonomous driver traffic.
Consider it in terms of human agency: People expect the use of "consumer electronics" and "appliances" to be mostly unpleasant, frustrating, buggy, full of arbitrary restrictions, and overmonetized.
Also, it seems like the amount of drivers on their phones, texting and driving or what have you, is at an all time high today. I work in an urban area and the amount of times I've missed a light because someone is on their phone stopped in the lane with a green has begun to give me legitimate road rage that I have never experienced in my life until now in my late 30's. I would trust the car more than the average person at this point.
It’s ok if the human can technically do better, so long as the machine is more likely to avoid severe failure
I don't buy into the hype, and I certainly don't buy what Tesla is promising.
Will most human drivers?
Every individual human would need to learn this separately from experience, which would require far more soccer balls (and children).
So the situation is really much worse than you describe. Even if we invested in demonstrating the "soccer ball and child" scenario for all driving students, they wouldn't be able to apply the experience to tennis balls and dogs, or a child entering the road without any sort of ball. Teaching people to drive would require an exhaustive course in every conceivable scenario that might arise while driving. You can see why it's an intractable problem.
The conclusion that a neural network classifies stuff 'correctly forever' is also not one supported by the current state of computer vision.
The problem with people is distraction. The problem with code (AI-ish) is people.
As for the second one, looks like the other car just passed in front of it.
I recall it mentioned somewhere that, after a well publicized tragedy hitting the side of a white truck on a bright day, Tesla was moving to radar?
And yet a lot of the cues this awesome video shows seems to be camera-based line detection etc?
Also interesting is the vision fps of ~13. That's 75ms latency.
https://arstechnica.com/cars/2018/06/why-emergency-braking-s...
In general, though, the art of combining information from multiple sensors is called sensor fusion and its a fairly well understood problem. Well, sometimes even good engineers make mistakes, see Schiaparelli and the saturated Kalman filter inputs, but we have a good idea of what those mistakes can be for different schema.
EDIT: Mistyped radar as lidar, fixed.