Robotaxis combine lidar, cameras and radar to build a continually updated model of the road. AI classifies objects, tracks their speed and direction, predicts what nearby people and vehicles may do, then selects and executes a driving maneuver through steering, braking and acceleration. The exact algorithms are proprietary and differ by operator, so this pipeline is a practical explanation rather than a universal technical specification.
The decision pipeline: from sensor readings to vehicle motion
A robotaxi does not “see” the road as a person does. Its software turns sensor measurements into structured outputs: object categories, positions, motion estimates, lane and signal information, predicted trajectories and a drivable path. Those outputs are refreshed continuously as the vehicle moves.
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- Sense: collect observations with the vehicle’s sensor suite.
- Perceive: detect, classify and track road users and relevant road features.
- Predict: estimate plausible next actions for nearby road users.
- Plan: choose a route, maneuver and safe path through the scene.
- Control: convert that path into steering, braking and acceleration.
Waymo describes this overall function as using sensor data, AI and machine learning to calculate a safe route and respond in real time to an evolving traffic environment (Waymo Help). Other operators may use different hardware, models and terminology.
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Each sensor provides a different type of evidence. Combining them is called sensor fusion: software compares measurements from multiple sources to produce a more reliable scene model than any single sensor could provide.
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| Sensor | Evidence it contributes | Qualification |
|---|---|---|
| Lidar | Three-dimensional shape and distance around the vehicle | Waymo’s description of its system; performance varies with conditions and objects. |
| Cameras | Visual detail, including signs, signals, road markings and object appearance | Useful for classification and semantic detail, but affected by lighting, glare and visibility. |
| Radar | Object speed and direction; additional measurements in difficult weather | Waymo says radar supports testing in rain, fog and snow; this is not a guarantee of perfect operation. |
Waymo says its lidar can measure object size and distance up to 300 metres around the vehicle. It also reports that its fifth-generation camera system can identify details such as pedestrians and stop signs at distances greater than 500 metres. These are Waymo-reported capabilities, not guaranteed detection ranges for every object, weather condition or robotaxi. No like-for-like independent comparison establishing which operator detects obstacles best is available in the cited material.
How perception turns measurements into a road-scene model
Perception software answers questions such as “What is there?”, “Where is it?” and “How is it moving?” Waymo’s 2021 Safety Report describes detection and classification of pedestrians, cyclists, motorcyclists, vehicles, traffic signals, construction zones and lanes blocked by cones. It also describes estimating an object’s state over time, including speed, heading and acceleration (Waymo Safety Report).
Tracking matters because one frame is ambiguous. A cyclist partly hidden by a parked car, for example, can be represented as a moving track whose recent positions and velocity help the system maintain continuity when the next sensor update is incomplete. The same scene representation can include static and semantic features such as lanes, traffic signals, cones and construction boundaries.
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Classification is not certainty. A detector can assign probabilities to several interpretations, and downstream software must account for uncertainty. Detecting an object also does not by itself produce a safe action; prediction, planning, control and operating limits still determine the outcome.
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How prediction estimates what people and vehicles will do
After perception identifies road users, a separate behavior-prediction component estimates plausible future movement. The Safety Report names behavior prediction as a distinct software function for modeling and predicting object intent.
Illustrative crossing example
If a pedestrian is approaching a marked crossing, the current position alone is insufficient. The planner needs possible near-future paths, such as continuing along the sidewalk or entering the crossing. A cyclist moving laterally toward the vehicle’s lane creates a similar need to evaluate several trajectories. These examples explain why prediction is necessary; they are not disclosures of a particular operator’s proprietary model outputs.
Prediction is probabilistic
Human road behavior is not deterministic. Software can maintain multiple possible trajectories and assign different likelihoods, then choose a maneuver that remains safe across the relevant possibilities. The exact prediction models, confidence thresholds and fallback rules are generally not public.
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How planning chooses a maneuver
The planner combines the scene model, predicted road-user behavior, map and route information, traffic rules and vehicle constraints. It may decide to continue, slow, stop, yield, change lanes, follow a temporary detour or maneuver around a blockage. A path must be drivable, respect nearby objects and leave enough margin for braking and steering.
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Occupied crossing
An illustrative sequence is to recognize a person in or entering the crossing, predict continued occupancy, select a stop or yielding trajectory, and command deceleration while maintaining a safe position. The vehicle can proceed only after the predicted conflict is resolved and the planned path is clear.
Blocked lane or construction
Cones, barriers or a stopped vehicle can change the available drivable area. Perception updates the lane and obstacle representation; prediction estimates the behavior of traffic around the blockage; planning then evaluates a legal path around it or decides to wait. If no confident path exists within the vehicle’s operating rules, stopping and requesting assistance may be safer than improvising.
How motion control executes the plan
Planning produces a desired path and speed profile. Motion-control software translates those targets into steering, throttle and brake commands. Feedback from the vehicle confirms whether it is following the trajectory and allows corrections for curvature, slope, tire behavior and changing obstacles. This is the final stage of the perception–prediction–planning loop, not an independent safety guarantee.
What happens when the system is uncertain
Uncertainty can come from glare, darkness, heavy rain, fog, snow, sensor occlusion, unusual objects, temporary road layouts or conflicting measurements. A robust system should represent that uncertainty rather than silently treating a weak interpretation as fact. Typical responses can include reducing speed, increasing following distance, yielding, stopping before a blocked area, choosing a more conservative route or escalating to a remote assistance process. Public descriptions do not disclose every operator’s thresholds or escalation logic, so exact behavior varies.
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Operating design domains limit where robotaxis make decisions
Autonomous driving is deployed within an Operational Design Domain (ODD): defined geography, road types, traffic conditions, weather assumptions, speeds and other constraints. Waymo says readiness decisions are specific to an ODD and its associated risks, considering safety methodologies alongside engineering and safety judgment (Waymo’s safety methodologies and safety readiness determinations).
An ODD explains why a robotaxi may operate reliably in one mapped service area yet be unavailable outside it or during conditions the service has not validated. It is not evidence that the vehicle can handle every road, climate or unusual event.
How to interpret robotaxi safety claims
Waymo’s Safety Impact hub presents a retrospective comparison of rider-only crash rates with human-driver benchmarks for surface streets. The company says it adjusts benchmark data geographically and updates information in line with NHTSA Standing General Order reporting timelines (Waymo Safety Impact). A retrospective crash comparison answers a different question from whether a new software or hardware release is ready for a new condition; Waymo describes prospective readiness as a separate process.
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In the United States, the National Highway Traffic Safety Administration says driverless testing, research and pilot programs occur in limited locations and conditions, while the agency monitors automated-vehicle safety through its Standing General Order. Its overview also distinguishes automated systems from driver-assistance features sold in consumer vehicles (NHTSA Automated Vehicle Safety). Regulations and deployment conditions differ by jurisdiction.
What public information does—and does not—establish
- Public Waymo materials describe sensor roles, perception outputs, behavior prediction, route planning and motion control.
- They do not provide a complete specification of proprietary neural networks, sensor-fusion equations, confidence thresholds or planner code.
- Waymo’s sensor-distance figures should remain attributed to the stated lidar and camera generations.
- One operator’s architecture should not be presented as the definition of all robotaxis.
- Detecting an obstacle does not guarantee avoiding it; safe behavior depends on the entire pipeline and the applicable ODD.
Common questions
What sensors do robotaxis use?
Sensor suites vary. Waymo publicly describes lidar, cameras and radar, with lidar supplying 3D distance and shape, cameras supplying visual detail and radar supplying speed and direction measurements.
Can robotaxis detect pedestrians and cyclists?
Waymo’s Safety Report lists pedestrians and cyclists among the road users its perception system detects and classifies. Detection capability is not a promise of perfect performance in every scene or condition.
Do all robotaxis use lidar?
Not necessarily. The cited sources describe Waymo’s configuration, not an industry-wide requirement. Operator comparisons require separately sourced hardware and performance evidence.
How far away can a robotaxi see?
Waymo reports lidar measurements up to 300 metres and fifth-generation camera identification of details beyond 500 metres. Treat both as vendor-stated capabilities tied to that system, not universal sight distances.
What does a robotaxi do when it cannot interpret an obstacle confidently?
The appropriate safety response is generally conservative—slowing, yielding, stopping or seeking assistance within the vehicle’s procedures. Exact triggers and recovery actions are proprietary and can differ by operator and ODD.
The Bottom Line
Robotaxis make driving decisions through a closed loop: sensors provide evidence, perception builds and tracks a scene, prediction estimates what others may do, planning selects a path, and control executes it. Sensor ranges and safety statistics must be read in their vendor, date, geography and ODD context; no public source here supports treating one operator’s design as the universal robotaxi standard.
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