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Robotaxis make driving decisions through a pipeline: sensors observe the road, perception software turns those observations into tracked objects and road features, prediction software estimates what nearby people and vehicles may do next, planning selects a maneuver, and vehicle control applies steering, braking, and acceleration. The exact algorithms and thresholds are proprietary and differ by operator, so Waymo’s published material is an illustrative architecture rather than a universal specification.
The robotaxi decision pipeline
A useful way to understand automated driving is to follow information as it moves through the vehicle. Each stage answers a different question:
- Sensing: What signals are arriving from the environment?
- Perception: What objects, road features and traffic controls are present, and where are they?
- Prediction: What might each relevant road user do next?
- Planning: Which route, lane change, stop, yield or avoidance path best fits the situation?
- Control: What steering, braking and acceleration commands will execute that choice?
- Continuous updating: What changed after the last observation, and should the decision be revised?
This is a conceptual model. Companies combine or subdivide modules differently, and public documents do not disclose every neural network, fusion method or safety threshold.
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Robotaxis do not rely on one perfect “eye.” Different sensors provide different evidence, and software combines the streams into a time-synchronized view of the vehicle’s surroundings. Waymo describes a suite containing lidar, cameras and radar; other operators may use different combinations or designs.
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| Sensor | Evidence it contributes | Published Waymo description | Important qualification |
|---|---|---|---|
| Lidar | Three-dimensional shape and distance | Waymo says lidar measures object size and distance around the vehicle, up to 300 metres. | The 300-metre figure is a Waymo-reported capability, not a guaranteed detection distance for every object, surface or weather condition. |
| Cameras | Visual detail, signs, signals, colours and appearance | Waymo says its fifth-generation camera system can identify details such as pedestrians and stop signs at distances greater than 500 metres. | This is a vendor statement about that camera generation; visibility and recognition depend on lighting, weather, occlusion and the object itself. |
| Radar | Relative speed and direction | Waymo describes radar as measuring object speed and direction and supporting operation in difficult weather such as rain, fog and snow. | Radar measurements still require interpretation and association with the rest of the scene; the description does not establish identical performance for all vehicles. |
Waymo’s explanation of these roles appears on its “How our cars drive” help page. Sensor fusion is valuable because each modality has weaknesses: a camera can provide rich semantic detail but is affected by lighting, lidar supplies geometry, and radar can provide motion-related evidence when visibility is reduced. The public material does not state the exact weighting, confidence thresholds or fusion architecture.
Perception turns measurements into a road-scene model
Raw points, pixels and radar returns are not yet a driving decision. Perception software detects and classifies objects, estimates their locations and tracks how those estimates change over time.
Objects the system may represent
Waymo’s 2021 Safety Report names pedestrians, cyclists, motorcyclists, vehicles, traffic signals, construction zones and lanes blocked by cones as examples of the scene elements its perception system handles. A production system can maintain attributes such as an object’s class, position, speed, heading and acceleration.
Tracking over time
A single frame cannot reveal whether a dark shape is moving, stopped or merely viewed from an unusual angle. Tracking links observations across successive sensor updates. The resulting state estimate can include position, velocity, heading and acceleration, along with uncertainty. As new observations arrive, the estimate is corrected; an object can be reclassified, split into multiple tracks or removed when it is no longer observed.
Road and semantic context
Perception also contributes a structured description of the roadway: lanes, traffic-control devices, construction boundaries, cones and blocked portions of the road. This context matters because the same physical object can require different actions depending on lane geometry, right-of-way rules and whether a temporary work zone changes the drivable area.
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Calling this a “scene understanding” system does not mean the vehicle understands the world exactly as a human does. It means software produces machine-readable entities, states and relationships that downstream components can use. Detection also does not guarantee a safe outcome; an object can be partially hidden, misclassified or behave unexpectedly.
Prediction estimates what other road users may do
After perception identifies relevant road users, a separate behavior-prediction function estimates plausible future movements. Waymo’s Safety Report describes this as modeling and predicting the intent of objects on the road.
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A pedestrian near a crossing may continue along the pavement, turn toward the curb or step into the roadway. A cyclist may hold a straight line, move laterally around a parked vehicle or merge into a lane. A vehicle approaching an intersection may brake, proceed or turn. The planner needs future possibilities, not only the object’s current coordinates.
Prediction is probabilistic, not clairvoyant
Public descriptions do not reveal the proprietary models or exact probability thresholds. Conceptually, the predictor can supply one or more possible trajectories with confidence estimates. Planning can then choose a manoeuvre that remains safe across credible possibilities instead of relying on a single optimistic guess. If observations change, predictions are recalculated continuously.
Planning chooses a route and immediate maneuver
Planning connects the evolving scene model to an intended action. Waymo says its AI and machine-learning software uses sensor data to calculate a safe route and respond in real time to changing traffic. In practice, planning can involve several time horizons:
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- Route level: selecting roads and turns to reach the passenger’s destination.
- Behavior level: deciding whether to continue, yield, stop, follow, merge or change lanes.
- Motion level: generating a drivable path and speed profile that respects road geometry and surrounding traffic.
For example, a lane blocked by cones may cause the planner to slow, determine whether an adjacent lane is usable, yield to traffic already there and generate a path around the obstruction. An occupied crossing may lead it to stop before the crossing and wait until the predicted conflict clears. These are explanatory examples of the pipeline, not claims about undisclosed event-by-event Waymo logic.
A decision is therefore more than “the camera saw an obstacle.” It combines object identity, position and motion; predicted behavior; lane and traffic-control context; the vehicle’s own speed and braking capability; and the rules of the operating environment.
Control executes the selected motion
Once planning selects a path and speed profile, motion-control software converts them into actuator commands. It regulates steering angle, acceleration and braking, then checks whether the vehicle is following the intended trajectory. Control runs in a feedback loop: the next sensor update can show that another road user moved, the road surface changed or the vehicle deviated from its target, prompting a revised command.
Waymo’s help page describes this relationship in its own words: “The Waymo Driver uses all that data to calculate a safe route with the help of AI and machine learning, allowing the car to respond in real time to the evolving traffic environment.” That is a vendor description of system behavior, not an independent certification of every driving outcome. See Waymo’s explanation.
Worked example: an unexpected blockage
The following sequence illustrates how the modules interact when a robotaxi approaches a lane blocked by a delivery vehicle. It is a teaching example, not a published trace of a particular ride.
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- Observe: lidar, cameras and radar produce new measurements of the lane, parked vehicle and nearby traffic.
- Detect and track: perception classifies the delivery vehicle, estimates its position and checks whether people, cyclists or doors are moving around it.
- Interpret the road: lane boundaries, cones, signals and the usable neighbouring lane are added to the scene representation.
- Predict: the system estimates whether nearby vehicles will continue, yield or occupy the gap needed to pass.
- Generate options: planning evaluates slowing behind the blockage, waiting, changing lanes or taking another route.
- Select a maneuver: the chosen option must fit traffic rules, available space and the vehicle’s motion limits.
- Execute and re-check: control applies braking, steering and acceleration while the entire pipeline updates with every new observation.
If confidence falls because the blockage, road markings or human movements are ambiguous, the public sources do not specify the exact proprietary fallback rule. They do make clear that readiness and operation are evaluated for defined conditions rather than for every imaginable road situation.
Why the operating domain matters
A robotaxi is not validated for an abstract road called “everywhere.” Its Operational Design Domain (ODD) defines the geography, road types, traffic situations, weather and other conditions for which the automated-driving system is intended to operate. Waymo says deployment readiness considers the relevant risks and outputs of its safety methodologies for a particular ODD, including hardware, automated-driving behavior and operations. Its overview is available at Waymo’s safety-readiness page.
That limitation explains why a vehicle can be highly capable in a mapped service area yet not be offered on every road or in every weather condition. Sensor performance, road design, construction patterns, traffic density and emergency procedures all change the risk calculation. A capability statement should therefore be read together with the service area and conditions in which it applies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret robotaxi safety evidence
Two kinds of evidence are often confused:
| Evidence type | Question it addresses | What to check |
|---|---|---|
| Retrospective safety comparison | How did vehicles perform in recorded operations compared with a selected human-driving benchmark? | Crash definition, exposure, geography, benchmark adjustment and reporting period. |
| Prospective readiness evaluation | Is a new release, capability or operating condition ready to deploy? | ODD assumptions, hardware and software evidence, engineering judgment, and the specific determination being made. |
Waymo’s Safety Impact hub describes retrospective rider-only crash-rate comparisons against human benchmarks for surface streets, with geographic alignment and updates tied to NHTSA reporting timelines. Waymo separately describes its prospective readiness process. One company’s comparison cannot establish a universal safety verdict for every robotaxi operator, city or weather condition.
In the United States, NHTSA’s automated-vehicle-safety overview says driverless testing, research and pilot programs take place in limited locations and conditions and distinguishes automated systems from driver-assistance features sold in ordinary vehicles. That is a US regulatory context, not a worldwide rule.
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Can robotaxis detect pedestrians and cyclists?
Waymo’s published Safety Report explicitly lists pedestrians and cyclists among the road users its perception system detects and classifies, and it describes estimating their motion over time. That establishes the intended perception function, not a guarantee that every person is detected under every combination of darkness, occlusion, weather, clothing, road geometry or sensor obstruction. Safe behavior still depends on prediction, planning, control and the applicable ODD.
How far away can a robotaxi “see”?
There is no single universal seeing distance. Waymo currently states that its lidar can measure object size and distance up to 300 metres around the vehicle, and that its fifth-generation cameras can identify details such as pedestrians and stop signs at distances greater than 500 metres. Both figures are Waymo-reported capabilities for specified hardware and should not be treated as guaranteed ranges for every object, condition or robotaxi. Detection, classification and reliable tracking are different accomplishments.
Do all robotaxis use lidar?
No universal sensor configuration defines a robotaxi. Public sources used here describe Waymo’s lidar, camera and radar suite, but they do not provide a like-for-like survey of other operators. Meaningful comparisons would need to examine each system’s sensor complement, perception outputs, prediction and planning disclosures, ODD and safety evidence. Without those comparable details, a brand ranking or claim that one sensor architecture is mandatory would be unsupported.
What the public still cannot know from these descriptions
Vendor explanations identify functions, not a complete software specification. They do not disclose every training dataset, neural-network architecture, sensor-fusion weight, confidence threshold, planner cost function, braking margin or handoff rule. Those details can affect behavior in rare and ambiguous situations. The defensible conclusion is therefore architectural: robotaxis use layered sensing, perception, prediction, planning and control, continuously updated within a defined operating domain. It is not a claim that a published page reveals every decision rule or that obstacle detection alone guarantees safety.
Quick Recap
Key takeaways
- Sensor streams provide complementary evidence rather than a single human-like view.
- Perception builds tracked objects and semantic road context, including motion estimates.
- Prediction considers how pedestrians, cyclists and vehicles may move next.
- Planning selects a route, behavior and path; control executes it through steering, braking and acceleration.
- Operating conditions and the ODD are part of the safety case, not an afterthought.
- Waymo’s ranges, architecture descriptions and safety statistics should remain attributed to Waymo and should not be generalized to every robotaxi.
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