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Electric car for £9500?

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coppice:

--- Quote from: nctnico on October 12, 2023, 06:27:56 pm ---
--- Quote from: coppice on October 12, 2023, 06:00:31 pm ---
--- Quote from: nctnico on October 12, 2023, 05:53:33 pm ---Throwing more sensors at a problem is not a solution in itself. The key problem to solve is to determine when the sensor data is correct and when not. That will take most of the computational power.

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Why do you think that? Do you have an reference models to base that on? Everything I've ever seen where results of sensing are solid and reliable use some form of sensor fusion specifically to resolve which data is correct. Biological systems do this to an extreme. Our own senses are very easy to fool in isolation, while the fused set works quite well, Most illusions are based on the senses not being able to fuse as they would otherwise do. There is hardly anything we get consistently right with just one sense. Good solid science and engineering work almost always relies on looking at things in multiple ways to resolve ambiguities.

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That is exactly what I wrote...

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It seems to be the opposite of what you wrote. You seemed to be emphasising that well thought out algorithms can do wonders without adding more sensors. The problem is this only goes so far. Our brain is using lots of the kind of heuristic algorithms you described, and it fails a lot. The flexibility needed to deal with a complex and unpredictable world needs multiple sources of information to resolve things.

mikeselectricstuff:

--- Quote from: tom66 on October 12, 2023, 12:05:32 pm ---What I don't fully appreciate with LIDAR is, when a substantial number of cars are running LIDAR, will there not be an issue with interference between similar LIDARs using similar wavelengths?  I presume there is some kind of CDMA or similar on the light pattern, but still could imagine eventually the additional light will reduce the sensitivity too much as it becomes difficult to separate 'your' code from 'their' code.  But maybe this limit isn't reached in practice.

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AIUI the duty cycle (in a particular direction) is very low - you want high peak power for optimum detectability, so this probably gives a reasonable head start in interference rejection. On top of that you can apply a lot of "reality checks" based on frame-to-frame changes based on likely velocities of real-world objects

Siwastaja:

--- Quote from: nctnico on October 12, 2023, 05:53:33 pm ---Throwing more sensors at a problem is not a solution in itself. The key problem to solve is to determine when the sensor data is correct and when not. That will take most of the computational power.

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Exactly. And different types of sensors, with different strengths and weaknesses, enables reliable sensor fusion. Algorithms are still not simple, but at least it's possible. I have zero trust on camera-only neural network stuff for anything serious like safety-critical obstacle avoidance. Adding a LIDAR does not magically make it happen, but it makes it possible. Computation is still a big risk item though.

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