Waymo vs Tesla: Two Ways to Build Self-Driving Cars

Article Author: Once the system holds a description of its surroundings, the next task is estimating what those objects will do. Several futures are valid at the same moment. For example, a cyclist approaching an intersection might continue straight, turn, or stop. A safe response should account for all possibilities. Waymo describes the system as producing many possible paths for each road user rather than one, drawing on accumulated driving data and accounting for the different ways a car, a cyclist, and a pedestrian move. A system that commits to one predicted future becomes fragile in situations where prediction matters most. However, carrying several weighted futures costs compute on every cycle, but can guard against the possibility of unusual situations.

Article Author: Waymo's Safety Impact hub reports 220.6 million rider-only miles through March 2026, meaning no human occupied the driver's seat for any of them. Measured against human crash rates in the same operating areas, adjusted for where within each city the service drives, the reported reductions are 94% for serious injury or worse crashes and 82% for injury-causing crashes. Tesla's Vehicle Safety Report takes a different form. It compares Teslas with Full Self-Driving (Supervised) engaged against Teslas driven manually, using the same telemetry pipeline for both, and reports 7 times fewer major and minor collisions and 5 times fewer off-highway collisions. The two reports answer different questions, and we can't simply do a side-by-side comparison. The difference comes down to what each population represents: Waymo is measuring outcomes across miles where no person was available to intervene. Tesla is measuring whether an assistance system reduces collisions while a driver remains responsible.

Article Author: 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.

Article Author: Something has to convert millions of pixels and points into a description that the system can understand and operate on. The nature and form of that description is one of the most important architectural decisions for an autonomous driving setup. The system maintains compact structured representations, meaning explicit lists of objects, their semantic attributes, and roadgraph elements describing lanes and connections. The Waymo engineering team provides three reasons for such a setup: Correctness and safety validation can run at inference time, while the vehicle is moving; Simulation runs efficiently at large scale, because a compact world state is cheap to replay and modify; Training feedback becomes verifiable, since a component evaluating driving quality has something concrete to measure. Tesla's documentation talks about per-camera networks performing semantic segmentation, which assigns every pixel to a category, plus object detection and monocular depth estimation, meaning distance estimated from a single camera. Those feed birds-eye-view networks that output road layout, static infrastructure, and three-dimensional objects in a top-down view. A full build involves 48 networks taking nearly 70,000 GPU hours to train and producing 1,000 distinct tensors per time step. The trade-off is that a structured representation can be inspected, logged, replayed, and checked against explicit criteria. But it limits what the system can express. On the other hand, a learned representation carries nuance no schema can anticipate.