Tesla: Automatic Labeling For Computer Vision
Summary
(Entropy quote at 13:45. Auto-labeling segment starts at 22:27.)
Andrej Karpathy discusses Tesla's use of weakly supervised learning for computer vision and/or imitation learning for planning (the two are kind of entangled):
Entropy and diversity of Tesla's datasets:
Auto-labeling using time:
Stuart Bowers describes shadow mode and imitation learning:
Summary
- Human driving behavior provides Tesla with a source of automatic labels for computer vision tasks related to autonomous driving.
- Automatic labeling allows Tesla to leverage its vast quantity of fleet miles. This gives it an advantage over competitors like Waymo and Cruise.
- Tesla can also use automatic labels for predicting road user behavior and performing driving maneuvers.
- Partially autonomous driving should not be overlooked as a source of higher revenue and gross margins for Tesla.
- Autonomy software will make the Cybertruck’s futuristic appearance feel more natural by the time it launches.
(Entropy quote at 13:45. Auto-labeling segment starts at 22:27.)
Andrej Karpathy discusses Tesla's use of weakly supervised learning for computer vision and/or imitation learning for planning (the two are kind of entangled):
Entropy and diversity of Tesla's datasets:
Auto-labeling using time:
Stuart Bowers describes shadow mode and imitation learning:
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