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Robotaxis navigate by repeatedly sensing the road, using AI to interpret what is happening and plan a safe path, then controlling the vehicle to follow it. Cameras, lidar, radar, maps and localization each contribute different information; no single sensor or AI model does the whole job. Because traffic and road conditions change from moment to moment, the vehicle runs this cycle continuously rather than choosing a route once and simply following it.
How do robotaxis see the road?
A robotaxi builds a changing picture of its surroundings from several sensors. Its software uses that picture to identify road users and features, estimate where the vehicle is, predict what other road users might do, and select a path. The car then acts on that plan and checks the scene again.
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Waymo describes its process as “Sense, Solve, Go.” The phrase is useful shorthand, but the work is a continuous feedback loop: a maneuver is planned from current observations, its control is adjusted as the scene changes, and new observations may lead to a different maneuver. A pedestrian stepping toward the curb, a car braking ahead or a newly visible obstruction can all change what the vehicle should do next.
Sense: gather observations
Exterior cameras, lidar and radar measure different aspects of the scene. Depending on the vehicle, other equipment may contribute too. Sensors observe lanes, traffic signals, vehicles, people and obstacles; they do not by themselves decide what action to take.
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Solve: interpret and plan
AI and other software combine sensor observations with the vehicle’s estimated position and map context. The system identifies objects, tracks how they move, estimates likely future behavior and chooses a route or a near-term trajectory. A route answers where to go; a trajectory describes the path the vehicle should follow over the coming moments.
Go: control the vehicle
Motion-control software translates the selected trajectory into commands for steering, acceleration and braking. It must keep the vehicle moving along the intended path while responding to updated observations. The next sensing and planning cycle follows immediately, so an earlier plan is not a guarantee that the car will continue unchanged.
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What does each sensor contribute?
Cameras, lidar and radar are complementary: cameras offer visual detail, lidar measures three-dimensional distance and shape, and radar measures range and motion. Their combination gives the software different ways to assess a scene when a particular view is incomplete or unreliable.
| Sensor | What it measures or reveals | Where it helps | Important limitation |
|---|---|---|---|
| Cameras | Visual detail such as traffic-light color, signs, lane markings, construction zones and the appearance of road users. | Interpreting the meaning of visible road features and distinguishing visual categories. | Glare, darkness, rain, grime and objects blocking the view can make visual interpretation harder. |
| Lidar | Laser-based distance measurements that form a three-dimensional point cloud of nearby surroundings. | Estimating object distance, shape and position, including the separation between objects. | It supplies geometry rather than all the semantic detail a camera can show; its usefulness depends on the sensor and conditions. |
| Radar | Range and velocity measurements for objects. | Tracking how fast something is moving and adding useful measurements in low light or some poor-weather conditions. | It complements rather than replaces visual and geometric information. |
| Other sensors | Depending on the vehicle, long-wave infrared cameras, microphones or other equipment may also be used. | Additional sensing modalities can contribute information beyond ordinary cameras, lidar and radar. | The exact equipment and role vary by vehicle; a sensor’s presence alone does not establish how it is used by the driving system. |
Sensor fusion is the software process of bringing these observations together. For example, a camera may reveal that a signal is red while radar reports that a vehicle ahead is slowing and lidar measures the distance to it. Agreement among different inputs can increase confidence in an observation; a disagreement can prompt the system to rely less on one reading or plan more cautiously. Redundancy is valuable because a sensor suite can continue to provide useful information when one view is weak, not because it makes every possible failure impossible.
Do self-driving cars use lidar or cameras?
Many robotaxi designs use both, often with radar as well, but there is no single sensor recipe for every vehicle. Waymo and Zoox describe multimodal sensor suites, while Tesla emphasizes processing visual data with a neural network. Those descriptions should not be treated as a controlled comparison of safety or capability: they do not establish that one approach works better in every operating condition.
Camera-focused systems must interpret the road from visual information, which can be rich in detail but may be affected by visibility. Lidar adds direct measurements of three-dimensional distance, and radar adds range and velocity measurements. A system using multiple modalities can cross-check some observations and gain alternative measurements when one sensor’s view is degraded. The software still has to interpret those readings correctly and decide what to do.
Manufacturer figures illustrate individual system claims, not industry-wide performance. Waymo’s explainer describes lidar coverage up to 300 meters and says selected camera details can be seen at greater than 500 meters; the page has a 2026 access year, but no publication date is stated. Waymo’s sixth-generation article describes centimeter-scale lidar range accuracy in urban situations and says its imaging radar tracks distance, velocity and size across lighting and weather. These details are specific to the cited Waymo descriptions and should not be applied to every robotaxi.
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Tesla’s Robotaxi Safety Report says each vehicle’s neural network processes over one million pixels every millisecond using anonymized visual data. The page was accessed in 2026 and does not show a separate publication date. This is a company-reported processing figure, not an independent measure of how safely a vehicle drives or a like-for-like comparison with another manufacturer.
How does AI know when to brake?
AI does not decide to brake from a single signal or a human-like understanding of danger. The driving stack estimates what is around the vehicle, tracks how objects are moving, predicts possible near-term interactions, and plans a path; motion-control software then uses the plan to manage speed and braking.
1. Detect and classify
Perception software processes sensor data to identify relevant objects and road features. It may need to distinguish, for example, a moving vehicle from a stationary obstruction or a person from other objects at the roadside. The confidence and quality of the underlying measurements matter: an object partly hidden from a camera is a different problem from one clearly visible across several sensors.
2. Track movement and predict possibilities
The system updates the estimated position and movement of detected road users as new observations arrive. Prediction software estimates how those movements could develop. That is not certainty about what another driver or pedestrian will do; it is a planning input that must be refreshed as more evidence comes in.
3. Select a safe, legal path and speed
Planning software weighs the vehicle’s intended route against immediate road conditions and predicted movement. If the available path or stopping space changes, the plan may call for slowing, stopping or taking another path. The software must consider more than whether the destination route still exists: the local trajectory has to fit the current scene.
4. Apply and adjust control
Motion control turns the plan into braking, acceleration and steering-equivalent commands. Waymo says its motion-control system provides smooth acceleration and braking after a path is selected. The control is repeatedly updated as new sensor observations arrive, rather than being a one-time command that assumes conditions will stay the same.
What role do maps and localization play?
A robotaxi uses localization to estimate where it is and map data to add road context. UK government guidance describes static high-definition maps as encoding road geometry, speed limits and street furniture; dynamic layers can add information such as congestion, crashes, flooding and weather. Positioning, navigation and timing data also help determine the vehicle’s location.
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Maps are not a substitute for sensing the road in front of the vehicle. Sensors check whether mapped features match current reality, which matters when a lane is closed, construction changes the road layout or an obstacle appears. The map helps supply context and foresight; current sensor observations help reveal what is actually there now. A disagreement between the map and the observed scene is a problem for the system to resolve, not a reason to assume the map is always right.
Localization and mapping also explain why a robotaxi’s operating area matters. A system must have enough knowledge and capability for the places and conditions in which it is used. A route-planning ability alone does not establish that a vehicle can operate safely on every road, in every weather condition or in an unprepared location.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can cameras alone navigate a robotaxi?
Camera-based navigation is an approach used by some companies, but the available company descriptions do not establish that cameras alone are sufficient for every robotaxi, road or condition. Cameras provide important semantic information, while lidar and radar provide measurements that can complement visual interpretation. Whether a particular design can handle a given situation depends on its complete sensing and software system, not just the sensor label.
When camera visibility is limited by darkness, glare, weather or obstruction, the scene may be harder to interpret visually. Waymo says lidar and radar provide redundancy when camera visibility is limited. That is a description of Waymo’s system, not proof that every multimodal system will respond identically or that backup sensing eliminates risk. Sensor fusion can reduce dependence on one input, but it cannot guarantee a correct interpretation or safe outcome in all circumstances.
What sensors are on a Waymo or Zoox car?
Waymo describes exterior cameras, lidar and radar, and its AI article also names external audio receivers. Zoox’s federal filing describes cameras, lidar and radar as well as long-wave infrared cameras, microphones and other sensors. The details establish that these companies document more than one sensing modality; they do not mean every vehicle configuration is identical or that all sensors serve the same purpose.
Waymo says its cameras provide a 360-degree view and can see traffic-light states. Its explainer describes lidar measuring object size and distance in 360 degrees and radar measuring an object’s speed and direction. The same explainer gives the lidar coverage and selected-camera-detail figures described above; they are claims from that Waymo page, not cross-company benchmarks.
For Zoox, the federal filing establishes the types of sensors it describes but does not provide equivalent range figures in the material summarized here. A fair comparison therefore cannot fill in a number from assumption. More generally, knowing a sensor list does not reveal its placement, data-processing design, operating limits or performance in a particular scenario.
Are robotaxis fully self-driving everywhere?
No. Robotaxi services operate within defined geographic and regulatory domains, and a vehicle’s ability to navigate a route does not mean it is authorized or capable of driving everywhere. The relevant operating area and conditions depend on the system and applicable permissions.
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In its consumer guidance, the U.S. National Highway Traffic Safety Administration states that no vehicle currently available for sale is fully automated or “self-driving.” That statement concerns vehicles available for sale to consumers; it should not be confused with whether a company may operate a robotaxi service under specific permits. Those are distinct questions, and the evidence here does not establish current permission status for every location or operator.
There is also no independent, dated, industry-wide crash-rate statistic established in the material used for this explanation. Manufacturer descriptions of sensors, AI processing or range are not substitutes for independent safety evidence, and a single company figure should not be generalized across robotaxi fleets.
Frequently Asked Questions
Do robotaxis use microphones to hear sirens?
Some systems document microphones or external audio receivers, but their presence alone does not show whether a vehicle detects sirens, how reliably it does so or what response it takes. The available descriptions establish audio equipment for some vehicles, not a universal robotaxi feature.
What does a robotaxi operating domain mean?
It is the set of conditions under which a particular automated-driving system is intended or permitted to operate, such as a defined service area or operating circumstances. A robotaxi’s ability to handle one route does not establish that it can handle roads and conditions outside that domain.
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The navigation process described here relies on onboard sensors, localization, maps and vehicle software, but the available evidence does not specify an internet requirement for every system. Connectivity and its role can differ by operator, vehicle and service.
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