141–150 of 550 posts
Interesting pieces: - First principles hardware design of focused self driving computer (many times better than any competing existing hardware). Already shipping in all newly produced cars. Currently working on next gen that will be 3x better to ship in a couple years. - Lidar is an unnecessary mistake that competitors are making that won't succeed (too expensive, need too many, unnecessary). - Real world fleet test…
Interesting pieces: - First principles hardware design of focused self driving computer (many times better than any competing existing hardware). Already shipping in all newly produced cars. Currently working on next gen that will be 3x better to ship in a couple years. - Lidar is an unnecessary mistake that competitors are making that won't succeed (too expensive, need too many, unnecessary). - Real world fleet test…
> Lidar is an unnecessary mistake that competitors are making This is very controversial and rest of the industry thinks exact opposite, many even claiming that Tesla is being irresponsible and even delusional in trying to do autonomy without lidar. The main points I have heard in favor of lidar are that computer vision is very flaky not only in suboptimal weather but even in good weather. Cameras are simply no where…
But only time will tell.
This focus on the hardware is silly. Assume for a second that their new hardware is 50x faster than their last hardware. That does not mean that their cars can self drive today. That does not mean that their cars can self drive three years from now. It's 100% not proven or obvious how car self driving skill and car self driving error rates scale with compute -- but it's surely not linear.
The thing I don't get about Tesla is why they keep heaping more on their plate rather than just (for large values of just) re-doing the motive power bit. That alone is a massive challenge and it is not something they've licked at economies of scale that allow a $10K vehicle to be brought to market, which is what it will take to really make this a success. Every extra they tack on to the base product is one they will…
Not if the company that cracks it eats its competitors or, assuming it's like waymo, commoditizes car manufacturers into suppliers for autonomous taxi fleets.
Earlier quoted context omitted.
Here's a snow cover map of North America on a random day in January: https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/U... Here is Europe and Asia on the same day: https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/E... For someone who lives in or close to that white part, "most scenarios" includes snow. Arguably for Level 4 autonomous cars in those areas, you either need to fully disable autonomy i…
You are comparing two different things. Old snow on the ground (your images) is not the same thing as active or recent snow accumulation on the vehicle (the original comment). It is completely reasonable for the car to disable autonomy due to snow accumulation until someone clears it off the vehicle. That says nothing about the autonomy of the vehicle with snow cover on the ground.
"No driver attention is ever required for safety (...) self-driving is only supported in limited circumstances (e.g. geofencing), and when these circumstances are no longer met the vehicle must be able to safely abort the trip, e.g. park the car, if the driver does not retake control."
How would that work if you're out driving on the highway, and it starts snowing hard so the car can't see anything? Just park on the highway?
If your car can't safely handle such a scenario, the automous feature would have to be "season-fenced" in addition to geofenced.
Earlier quoted context omitted.
> Lidar is an unnecessary mistake that competitors are making This is very controversial and rest of the industry thinks exact opposite, many even claiming that Tesla is being irresponsible and even delusional in trying to do autonomy without lidar. The main points I have heard in favor of lidar are that computer vision is very flaky not only in suboptimal weather but even in good weather. Cameras are simply no where…
They pair it with a forward facing radar which is inexpensive and good at depth perception. Elon predicts all competitors will eventually drop lidar. He mentions it's expensive, but also not as good in a lot of cases (and all roads/signs are designed for vision). He argues that getting vision to work is a prerequisite for getting self-driving to work and once you have it working, lidar is worthless (and unnecessary).
Case in point: both the Gigafactory (vastly overbuilt for the quantity of batteries actually produced) and the Alien Dreadnought (vastly overbuilt for the number of cars Tesla current produces...assuming that Tesla is ever able to get the fancy automation working). Boring Co digging a two-mile tunnel in West LA without bothering to learn how to pour concrete smoothly, or to make the "rails" the proper width, or learning about ventilation, or access points....
Karpathy's presentation is really good. Watch it later if you get some time. The key points are - Telsa is using fleet for learning (Fleet learning). This has 3 components, a. Trigger infrasture that collects the kind of data telsa is looking for training. The rational here is we don't need massive amounts of same kind of data but needs right kind of data for training neural networks correctly. b. Data Engine which l…
Does anyone doing lidar not also do visual recognition?
Earlier quoted context omitted.
And would you drive if snow covered your windscreen? Or would you clean it? Which is obviously possible for cameras too.
The windscreen has wipers, on the Tesla model 3, I don't think all the sensors have wipers... so that's an issue.
Simple physical solutions are available if necessary for the cameras too. I don't have a tesla so I don't know what measures they use to keep the cameras clean but I'm sure they've thought about it and designed for it.
Karpathy's presentation is really good. Watch it later if you get some time. The key points are - Telsa is using fleet for learning (Fleet learning). This has 3 components, a. Trigger infrasture that collects the kind of data telsa is looking for training. The rational here is we don't need massive amounts of same kind of data but needs right kind of data for training neural networks correctly. b. Data Engine which l…
From working with some different types of photo sensors (CCD, avalanche photodiodes, photomultiplier tubes), you might imagine that incident light from another car's emitter would be much brighter than the reflected light the lidar sensors are reading and cause saturation.