3 ms·

Why not just have a system that produces a full 3D model of the surroundings and their velocity (using something like doppler radar) and then just do a geometri

by devit 9y ago

Why not just have a system that produces a full 3D model of the surroundings and their velocity (using something like doppler radar) and then just do a geometric computation in 4D spacetime to find out a path that doesn't intersect with the trajectory of anything? (assuming their speed is unchanged) That should prevent all such trivial accidents and allows to actually mathematically prove that the software works, as it should be required for such a critical system. Machine learning can be added for object detection to allow more sophisticated trajectory prediction.

Crespyl 9y ago
Indeed, "why not just"; I can't tell if you're being sarcastic or not. I'll assume the latter: Even if you were able to perfectly maintain an accurate 3D model of the surrounding geometry (and associated trajectory of all moving objects), it doesn't mean you have enough information to make 100% accurate predictions. For example, a frozen over road often has very nearly exactly the same physical geometry and color, but extremely different behavior to cars trying to drive on it. Other weather effects like fog or snow bring their own challenges. You're right though, that precise and accurate 3D models do do a lot to make driving easier and more correct, the problem is that the hardware necessary to produce those models is still very expensive (LIDAR). Most (all?) of the advanced self-driving prototype cars use LIDAR, while Tesla has decided that they can do without for their consumer models, sticking with just the simpler and less expensive RADAR and cameras. In theory, Tesla's right, since humans get by just fine with only two "cameras", but we're clearly a long way from automating that level of performance.
- devit 9y ago
  Using multiple separate emitters and detectors it should be possible to determine how every point reflects light (i.e. sample its BRDF for a limited angular range and set of wavelengths), which should allow to distinguish at least between a limited set of materials such as snow, solid ice, liquid water and asphalt. Cameras alone seems unsuitable, since it doesn't seem possible to make a camera-based system that works correctly all the time, ideally provably so, as required for a life-critical system.

by devit 9y ago

Crespyl 9y ago

Indeed, "why not just"; I can't tell if you're being sarcastic or not. I'll assume the latter: Even if you were able to perfectly maintain an accurate 3D model of the surrounding geometry (and associated trajectory of all moving objects), it doesn't mean you have enough information to make 100% accurate predictions. For example, a frozen over road often has very nearly exactly the same physical geometry and color, but extremely different behavior to cars trying to drive on it. Other weather effects like fog or snow bring their own challenges. You're right though, that precise and accurate 3D models do do a lot to make driving easier and more correct, the problem is that the hardware necessary to produce those models is still very expensive (LIDAR). Most (all?) of the advanced self-driving prototype cars use LIDAR, while Tesla has decided that they can do without for their consumer models, sticking with just the simpler and less expensive RADAR and cameras. In theory, Tesla's right, since humans get by just fine with only two "cameras", but we're clearly a long way from automating that level of performance.

devit 9y ago
Using multiple separate emitters and detectors it should be possible to determine how every point reflects light (i.e. sample its BRDF for a limited angular range and set of wavelengths), which should allow to distinguish at least between a limited set of materials such as snow, solid ice, liquid water and asphalt. Cameras alone seems unsuitable, since it doesn't seem possible to make a camera-based system that works correctly all the time, ideally provably so, as required for a life-critical system.