But the Autopilot has cameras, and therefore it has 3+1D data (2D frames, 1D inferred depth from stereo or training, 1D time from frame diffs). Even with a 30fps frame rate at 60mph, two frames from a video feed would see the truck move closer by a meter. The network can be trained to distinguish a truck in the lane from an overpass, so those two frames should be sufficient to signal danger. Instead, the car accelerated over 70 meters before the human, again using just her eyes, applied the brakes.

This is an edge case, and clearly something that requires more training to avoid. The challenge in edge-case learning is to encounter them often enough, or perhaps create them artificially, and then to ensure the learning can be transferred and generalized to all similar situations.