481–490 of 658 posts
Not sure I'd trust an autopilot based on crude visual odometry and segmentation that can only achieve a paltry 17 fps.
Compare Google's system from five years ago.[1] (at 7:42) This new video is at n00b level compared to that. [1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
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Seems like it would make more sense to model the inertia. Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed. Though they should have a model for detecting obstacles in the view regardless of inertia, because sometimes something really does appear in front of you in a thirteenth of a second. You could probably model inertia with n prior frames of probability fields.
> Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed What if they are hit by a truck? Maybe not 100,000 m/s^2 but if you assume that cars can't accelerate in directions they aren't pointed, you will be wrong at the worst possible time.
Any other HN users getting an Access Denied on their website? The usual "You don't have permission to access " http://www.tesla.com/" on this server." I'm not behind any proxies - I'm at home, connecting from Brazil here.
Same here. Tesla's website is blocked in Brazil by Tesla itself.
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Your human eyes+brain do the same thing.
Our brains are purpose-built to really see only the very core center of our vision; the brain then creates an approximate model of the surrounding space, but a lot of what is in that model is influenced by what the brain "expects" to see. So I think yes, we can also suffer from some similar inaccuracies when the objects in question are in our periphery. However the main difference is that, when we are consciously loo…
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.
Compare Google's system from five years ago.[1] (at 7:42) This new video is at n00b level compared to that. [1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
Wow. This is what I’d expect from a large company. Full 3D representations with super smooth object recognition. Tesla’s looks like a student project compared to that. 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.
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Isn't that asking for too much? Most humans can't drive safely in blizzards. And unlike self-driving cars, many humans are overconfident in their abilities. If a self-driving car recognizes dangerous conditions (poor visibility, malfunctioning sensors, etc), it can simply stop. It seems as long as these machines are safer on average than humans, they're worth deploying.
Really? By your logic Minnesota, Iowa, North Dakota, South Dakota, Colorado, Montana, Idaho, Wisconsin, etc. should shut down for the winter? Businesses don't shut down just because the forecast says it might snow. Plows can't get out fast enough to clear the road to a point the lines would be readable/visible for a self-driving car. Humans may not be perfect but we find a way to get home in the snow without the mass…
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Isn't that asking for too much? Most humans can't drive safely in blizzards. And unlike self-driving cars, many humans are overconfident in their abilities. If a self-driving car recognizes dangerous conditions (poor visibility, malfunctioning sensors, etc), it can simply stop. It seems as long as these machines are safer on average than humans, they're worth deploying.
> Most humans can't drive safely in blizzards. Sure they can. During snow squalls, accident rates obviously go up (especially if the roads are icy or if it's the first one of the season), but most drivers are good at assesing risk and take appropriate actions. Importantly, they have the ability to decide that it's worth the risk to go out on the roads to pick up Sally from daycare (for example). In any case, if the c…
I lived in snowy climates for a decade. Your summary completely disagrees with what I experienced. In whiteout conditions there were always cars that had gone off the road. Even after years of driving in the snow, people get into accidents far more often than in dry conditions.
And don’t forget: most of the US population has little to no experience driving in winter conditions. If the Bay Area got an inch of snow, there would be more collisions than a demolition derby.
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I've gotten the impression that the "firing people in weird and capricious ways" bit has been greatly exaggerated. I do think anyone taking this has to expect grueling and challenging work. The job description practically demands that.
I don't doubt for a second that people like Musk (and before him for example Steve Jobs and Jack Tramiel) can be very demanding and sometimes difficult to work for. I also don't doubt for a second that a lot of the stories told have a sour grapes quality to them: If you're good at the kind of job they're offering, you can easily find employment somewhere else and you're also probably smart enough to move on of your o…
I kind of giggle because the jittery-ness of some of the graphics makes it like I'm watching some robot version of Home Movies or Dr Katz. 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…
The raw sensory inputs to a human brain must also be jittery. Eyes apparently see highest definition detail only in a tiny area of the visual field, for example. The brain stitches it together, interpolates, tags features, and guesses. The preprocessing miraculously creates the impression of a big clear image out of a dirty data feed from two jello cameras that are swiveling around all the time. There's a lot a smoot…