It is easy to underestimate how much one relies on senses other than vision. You hear many kinds of noises that indicate road surface, traffic, etc. You feel road surface imperfections telegraphed through the steering wheel. You feel accelerations in your butt, and conclude loss of traction from response of the accelerator and motion of the vehicle. Secondly, the human eye has much more dynamic range than any camera. And is mounted on an exquisite PTZ platform. Then turning to the model -- you are classifying obstacles and agents at a furious rate, and making predictions about the behavior of the agents. So, in part I agree that the models need work, but the models need to be fed, and IMHO computer vision is not a sufficient sensor feed.

Consider an exhaust condensation cloud coming from a vehicle's tail pipe -- it could be opaque to a camera/computer-vision system. Can you model your way out of that? Or is it also useful to do sensor fusion of vision data with radar data (cloud is transparent) and others like lidar, etc. A multi-modal sensor feed is going to simplify the model, which in the end translates into compute load.

No, I don't think that will be successful. Consider a day where the temperature and humidity is just right to make tail pipe exhaust form dense fog clouds. That will be opaque or nearly so to a camera, transparent to a radar, and I would assume something in between to a lidar. Multi-modal sensor fusion is always going to be more reliable at classifying some kinds of challenging scene segments. It doesn't take long to imagine many other scenarios where fusing the returns of multiple sensors is going to greatly increase classification accuracy.

Thanks! That is much farther back than I thought, even 200 kYA.

When is the first evidence for cooking?

I have a wallet with a pocket for an airtag.