Waymo's dual-component verification — where a generative model and a separate validation layer must both agree before the vehicle moves — differs fundamentally from Tesla's approach, where a human driver serves as the verification layer under Full Self-Driving (Supervised).

Waymo trains large Teacher models distilled into real-time Student models, then validates their outputs through a separate onboard layer, while Tesla's current consumer system relies on attentive human drivers enforced through a strikeout mechanism. ✦ AI generated

Article Author · ByteByteGo Newsletter · 2026-08-17 · original ↗

Waymo trains large Teacher models to generate safe, comfortable, and compliant action sequences. It then distils them into smaller Student models sized to run onboard in real time. Distillation transfers behaviour from a large model to a compact one. Output from that Student model then passes through a separate onboard validation layer, which verifies the trajectories the generative model produced. This means that two independent components have to agree before the vehicle moves. For Tesla vehicles on the road today, verification comes from a person. Full Self-Driving (Supervised) requires an attentive driver and leaves the vehicle slightly short of autonomous. The system enforces this through a strikeout mechanism, where repeated inattention warnings disengage the feature for the remainder of a trip. Enough strikeouts suspend access for a week.

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