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You’re right. comma.ai is making the same mistake and it shows in their current experimental mode. I find the argument that you can allow both longitudinal and

You’re right. comma.ai is making the same mistake and it shows in their current experimental mode. I find the argument that you can allow both longitudinal and lateral control to be expressed by a black box difficult to accept. The proof of the driving is in the handling. There’s a video of an engineer at comma.ai mocking self-driving teams for semantically labeling objects on the road in SAE Level 2 and beyond systems. Which screams stupid to me. Why would you be proud that your system has no idea what a pedestrian is. Or what a traffic cone or a speed limit sign are? Idiotic. At least Tesla still semantically labels. But I argue that you can’t get smooth driving out of neural network controls because they inherently have no idea what smooth driving explicitly is. They estimate it. The best they can do is attempt to mock it, which without programmer-defined rails WILL result in excessively high jerk and acceleration numbers, as proven by openpilot’s implementation.

zaptrem 3y ago
The problem is manual labelling doesn't scale. Switching to an unsupervised approach is what made many huge AI advances over the past couple years possible since we're mostly limited by compute now. For Tesla (who have practically unlimited driving footage) this is particularly advantageous.
- andrewmcwatters 3y ago
  It’s not the labeling itself, it’s whether or not you’re allowing unfiltered longitudinal control to originate from neural nets. Good but erratic lateral control at worst results in hugging a lane line. Anything less than perfect longitudinal control means the driver will toggle it on and off during portions of traffic where it won’t follow the flow of traffic closely enough, or if it follows to closely, or if you’re experiencing a high cabin jerk factor. You cannot screw up longitudinal control. It’s nearly the whole experience.
  - zaptrem 3y ago
    I think this is what end-to-end models will be best at, since they'll be able to account for far more environmental factors than a manually written planner flipping between the stop light deceleration curve and car-ahead curve constantly, for example.

zaptrem 3y ago

The problem is manual labelling doesn't scale. Switching to an unsupervised approach is what made many huge AI advances over the past couple years possible since we're mostly limited by compute now. For Tesla (who have practically unlimited driving footage) this is particularly advantageous.

andrewmcwatters 3y ago
It’s not the labeling itself, it’s whether or not you’re allowing unfiltered longitudinal control to originate from neural nets. Good but erratic lateral control at worst results in hugging a lane line. Anything less than perfect longitudinal control means the driver will toggle it on and off during portions of traffic where it won’t follow the flow of traffic closely enough, or if it follows to closely, or if you’re experiencing a high cabin jerk factor. You cannot screw up longitudinal control. It’s nearly the whole experience.
- zaptrem 3y ago
  I think this is what end-to-end models will be best at, since they'll be able to account for far more environmental factors than a manually written planner flipping between the stop light deceleration curve and car-ahead curve constantly, for example.