Tesla: Automatic Labeling For Computer Vision

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.

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I just had an interesting thought re: weakly supervised learning. What if people have a distinctive way of stopping for stop signs? Can you deep learn the behaviour of stop sign stopping and use that as a weak signal for when a stop sign is present in the cameras’ field of view?

Maybe the way people stop for stop signs is similar to the way they stop for stopped cars up ahead (e.g. at a red light or in stop-and-go traffic on the highway). In that case, maybe the “behavioural signature” of stopping for a stop sign could be combined with other cues, such as the presence of a single solid yellow line (or other cues indicating a city/town/suburban street) and the absence of a traffic light.

Big picture: if you can correlate certain recognizable driving behaviours to certain visual phenomena, you have a source of weak signal for weakly supervised learning. You can, therefore, train computer vision tasks using automatic labels.