- If radar/lidar fails, vision needs to work. If vision works, radar/lidar are not needed.
- Sensor fusion is hard: outliers in radar data ended up hurting more than the non-outliers helped.
The second one sounds to me like the real reason: Sensor fusion is not trivial and if it doesn't work correctly, you'll get the worst of all worlds.
If radar/lidar fails, it's perfectly fine for vision to work just barely enough to slow down for a few seconds while telling you that you're going to have to self-drive yourself.
Their vaccine is not very effective against omicron, and they have far less hospital beds per capita than the west. So a 'flatten the curve' strategy will cause a lot more deads than it has in Europe and the USA.
I don't think the number of deaths is the problem now but the speed at which they occur. If we assume about 0.2% death rate for China on average that would almost be 3 Million. Of those 1.5 Million would occur in 4 weeks (assuming speed is similar to Hong Kong and lower medical capacity). That's 50000 dead per day on average. Especially in the first half it's scary because the number just keep on rising to the sky with a peak of 100000 per day (or more? we don't know until they fall again).
The difference in effort between keeping that kind of curve flat and ZeroCovid is small. I would even assume that ZeroCovid is easier than flattening the curve. While ZeroCovid is not an exit strategy in some areas it works better while waiting for an effective vaccine or some other exit strategy. So presumably that's what they do right now.
The Eigen library (https://eigen.tuxfamily.org) has great attention to numerical robustness and accuracy in it's implementation.
The documentation is good, but for the numerical discussions, you'd have to read the source code.
http://ceres-solver.org/ works well, in my experience.
It does require code to be templated, which might increase computation time.
It is possible to explicitly define derivatives for individual functions, this can help preventing numerical instabilities in corner cases.
The detectors themselves, to reduce thermal noise. It is not uncommon to cool down sensitive detectors, for example, in electron microscopes, the x-ray detectors are typically cooled down below -40 Celsius.
Taking overcapacity for wind free periods and energy storage into account, the numbers are less favorable, but transitioning to green energy doesn't really need to affect our wealth or lifestyle all that much.
_-_renewable_energy.svg){kind=link}
- If radar/lidar fails, vision needs to work. If vision works, radar/lidar are not needed. - Sensor fusion is hard: outliers in radar data ended up hurting more than the non-outliers helped.
The second one sounds to me like the real reason: Sensor fusion is not trivial and if it doesn't work correctly, you'll get the worst of all worlds.