4 ms·
I feel the other reason is that Tesla has not figured out a way to put Radar into their ML pipeline. If you take the Range-Doppler Map from the radar as the 'pi
by 3apo 4y ago
I feel the other reason is that Tesla has not figured out a way to put Radar into their ML pipeline. If you take the Range-Doppler Map from the radar as the 'pixel' map, that data is inherently very dependent on the scenario and the radar sensor intrinsic parameters. This variability in what the radar sees in the RD space is what makes this a challenge for ML/AI pipelines.
If Tesla were to 'fuse' information from these sensors in the object track level - I believe they will be less susceptible to this variability.
- dreamcompiler 4y agoExactly. Radar gives you direct range data; camera pixels need to be processed by ML to infer range data, and the latter is never going to be as close to ground truth as the former, so the former should be prioritized.
- Nomentatus 4y agoNot quite. Light waves are so short you'll get some return from almost any surface, because the surface is rough at the scale of such a small wavelength. This isn't true of radar, and it's not just what substance the outbound radar hits but how flat it is, too. You may get no return. Or almost none. Even smooth, round steel posts give very little return IIRC. There's also an echo problem with long waves such as sound and radar, particularly in urban areas. In which case what you think is a firm direct return may be a very indirect return that happens to be in synch with the signal you were expecting.
- touch_abs 4y agoIts interesting, that kind of object level fusion is a fairly different problem to training visual perception, following some of the less in fashion robotics techniques. I wonder if its a case of the Tesla engineers focusing on the fad technologies (or just their strengths) more than its a hardware cost thing.