This storyline collects Waymo patents on sensing and prediction: hearing sirens through microphones, adapting lidar to fog, spotting hidden pedestrians and wobbly cyclists, and filtering out glare from reflective signs. Together they show Waymo building layered awareness so its robotaxis can react before a hazard becomes visible.
40 filings · tracking since Apr 2026 · latest May 2026 · updates automatically as new filings publish
US 2026/0131781 A1
A robotaxi that spots danger between other road users can slow or steer clear before wreckage spreads into its path, turning prediction of third-party crashes into collision avoidance for itself.
US 2026/0189775 A1
A robotaxi that sees obstacles in complete darkness gives it a real edge over human drivers who depend on headlights alone.
US 2026/0186111 A1
After establishing how robotaxis need to see through fog and darkness, this filing shows the sensor hardware itself: a custom amplifier that lets lidar handle bright and dim reflections at once, the way human eyes adjust to sudden changes in light.
US 2026/0185687 A1
Uneven infrared output would blind night-vision cameras in dark conditions. Waymo's rotating test rig catches these flaws before lights go into cars, ensuring consistent sensor performance across all angles.
US 2026/0186482 A1
Incremental forward creeping controlled by remote operators lets the car gather sensor data around obstructed corners without committing to a full crossing, extending the robotaxi's ability to move through situations where its own perception hits a wall.
US 2026/0187321 A1
A robotaxi's software needs validation before deployment, and replaying thousands of recorded road scenarios lets Waymo verify that new code produces the same steering, braking, and decision points as proven driving behavior.
US 2026/0189798 A1
The storyline has shown how Waymo's sensors need to work together to see clearly. This filing reveals how lidar actively protects cameras from overexposure when bright light floods the scene.
US 2026/0179491 A1
A robotaxi that knows where parking exists before arriving eliminates the circling phase entirely, letting the car navigate straight to an available spot or wait efficiently rather than burning time on search.
US 2026/0181274 A1
After building a car that listens and predicts, Waymo now sharpens what it actually sees: a camera that locks exposure faster than human eyes adjust to sudden light changes.
US 2026/0175834 A1
Detecting physical failures in the vehicle itself, flat tires, cargo shift, trailer misalignment, fills a blind spot in autonomous sensing that exists beyond the road ahead.
US 2026/0179389 A1
Three specialized cameras covering near, mid, and far ranges let the system detect both curb-side pedestrians and distant obstacles in one unified view, filling gaps that single-lens designs leave blind.
US 2026/0169124 A1
A robotaxi that can't trust its own sensors is grounded. This filing shows how Waymo plans to keep radar working as a reliable input by catching degradation before it turns into blind spots.
US 2026/0170704 A1
The prediction layer fills in what the car needs after it perceives the scene: given what cameras and lidar see right now, what does the road hold in the next few frames. That forecast feeds into planning the safe next move.
US 2026/0170824 A1
Recognizing objects at the sensor itself cuts the latency and bandwidth needed to process what a robotaxi sees. This shifts the computational load from the car's central computer to individual cameras, each running its own AI model suited to its viewing angle.
US 2026/0167232 A1
A unified prediction model that plans the car's path and forecasts other vehicles' movements in one step, eliminating the lag that comes when separate systems must sync their outputs.
US 2026/0159135 A1
Tracking a pedestrian's 3D skeleton in real time lets the car spot weight shifts and shoulder angles that precede crossing, catching intent before the person moves.
US 2026/0153357 A1
Comparing live sensor data against stored map geometry lets the car catch a drifted camera or lidar without needing a service visit, filling a gap in the perception pipeline that earlier patents assumed was already solved.
US 2026/0156719 A1
A robotaxi that can't see through frost becomes a liability. Dual heating zones let Waymo keep critical sensors clear without the power drain of uniform heating across the entire window.
US 2026/0154935 A1
After showing how robotaxis can see through obstacles, this filing reveals how they measure visibility itself, using camera and lidar data to gauge fog and rain density rather than guess from appearance alone.
US 2026/0153610 A1
Radar crosstalk between nearby autonomous vehicles creates false detections and blind spots. Waymo's solution assigns each vehicle a distinct code sequence so their signals don't interfere, letting multi-car fleets operate safely in tight urban spaces.
US 2026/0145694 A1
Keeping the car moving safely when onboard computers fail requires two independent computing systems that can each steer the vehicle to a safe stop, confirming Waymo's bet that redundancy matters more than a single powerful processor.
US 2026/0145711 A1
Filtering which nearby agents matter most before computing their paths cuts the neural network's processing load, letting predictions run faster on the robotaxi's onboard hardware.
US 2026/0147122 A1
A robotaxi that sees dead zones around it would gain confidence in murky conditions. This filing shows how to convert wasted sensor data, light the LiDAR already captures but ignores, into a makeshift map of whatever its main laser pulses miss.
US 2026/0145703 A1
After showing how robotaxis sense hazards on the road, Waymo now maps what happens when they leave it. This patent puts the vehicle itself in charge of finding the right maintenance bay, turning depot logistics into a navigation problem the car solves solo.
US 2026/0141246 A1
A robotaxi that narrates what it observes can feed those descriptions into prediction models, giving the system a way to reason about unfolding situations using language as an intermediate step rather than jumping straight from sensor data to forecasts.
US 2026/0141702 A1
After mapping what sensors see, Waymo now anchors those readings to language so the car understands scenes the way humans describe them.
US 2026/0140979 A1
Predicting pedestrian intent requires the car to answer specific questions about sensor data in real time, moving beyond preset threat scenarios.
US 2026/0138647 A1
The prediction theme extends beyond traffic and weather: Waymo is now working to spot the instant a cyclist or pedestrian begins to lose balance, giving the car milliseconds to brake before impact rather than reacting after someone has already fallen.
US 2026/0140242 A1
Lidar's vulnerability to retroreflectors, surfaces designed to bounce light straight back and saturate the sensor, requires early detection so the car can adjust its laser power before losing visibility of actual obstacles nearby.
US 2026/0138621 A1
A localized vision-language model lets the car's planning and prediction systems query specific road regions directly, avoiding the computational cost of re-analyzing entire camera feeds each time a subsystem needs clarification on what it's seeing.
US 2026/0140505 A1
A robotaxi that learns from rare edge cases needs exposure to them. This filing shows how Waymo routes its fleet to deliberately seek out the specific scenarios its system still struggles with, rather than hoping chance encounters provide enough training data.
US 2026/0138641 A1
After building a car that sees and predicts, Waymo now ensures it can execute safely: a real-time grip calculator that lets the robotaxi know its own limits on wet roads before it commits to a maneuver.
US 2026/0141307 A1
Predicting hydroplaning requires knowing not just that rain fell, but how much water actually clings to pavement. Waymo's deep learning model measures surface wetness in real time, letting its robotaxis adjust behavior before losing grip.
US 2026/0141729 A1
Two cameras watching the same brake lights from different angles let Waymo filter out reflections and dirt, confirming the robotaxi needs multiple viewpoints to trust what it sees on the road ahead.
US 2026/0133317 A1
Pairing distance to velocity readings stops the sensor from assigning a cyclist's speed to a bus or vice versa, a confusion that could send the car's prediction system wildly off course at intersections.
US 2026/0131789 A1
Generating multiple lane-change paths and ranking them by predicted driver behavior adds a prediction layer to the robotaxi's decision-making, moving beyond static obstacle detection.
US 2026/0131805 A1
After learning to see through fog and hear sirens, the robotaxi now reasons about what lurks beyond its line of sight, assigning risk scores to invisible pedestrians based on context clues.
US 2026/0129360 A1
Microphone arrays that detect siren frequencies let the robotaxi respond to emergency vehicles before they enter the camera's field of view, compressing the reaction delay that plagues lidar-and-camera-only systems.
US 2026/0129148 A1
A robotaxi that rewires its own sensors when fog rolls in would stop guessing and start measuring what it can actually see in real conditions.
US 2026/0118129 A1
Converting raw sensor streams into usable maps has meant separate pipelines for different data types; this filing shows how a single transformer model can fuse camera, lidar, and radar directly into steering commands.
A good chunk of these filings deal with raw sensing. Waymo patents lidar that adjusts to fog in real time, a hybrid lidar that separates speed from distance when several objects overlap, and a lidar filter that screens out blinding retroreflectors from road signs and bike reflectors. A microphone system teaches the car to hear an ambulance before a camera spots one. On the software side, a transformer-based model builds navigation maps and a vision-language model lets different car systems ask questions about what is happening on the road ahead.
Another cluster targets prediction. One patent guesses where a hidden pedestrian might step out from behind a parked bus. Another watches for cyclists about to lose their balance. A separate system predicts crashes between two other vehicles, not just the ones involving the robotaxi itself. Waymo also patents a machine learning system for judging lane changes and a real time acceleration envelope for deciding how hard the car can brake or speed up. A targeted data collection patent sends cars out to hunt for the situations these models need to learn from.
Read together, the filings point to Waymo pairing better sensors with models that guess intent, not just detect objects. The recurring theme is anticipation: hearing sirens early, reading fog, guessing a pedestrian's next step, judging a cyclist's balance. Readers should watch for patents that stitch these pieces together, letting the car's perception, prediction, and driving decisions run off shared models. As always, a patent filing shows where Waymo's engineers are experimenting, not what will ship in a production robotaxi.
It follows Waymo patent filings about how its self-driving cars sense the world and predict what people and other vehicles might do next, from hearing sirens and reading fog to guessing when a hidden pedestrian or wobbly cyclist could move into the road.
Not necessarily. A patent shows a system Waymo has designed and filed for legal protection, which points to research direction. It does not confirm the feature is running in current robotaxis on public roads.
Several patents in this storyline model hidden pedestrians, cyclists losing balance, and crashes between other vehicles. That pattern suggests Waymo is spending real engineering effort on anticipating danger before it happens, not just reacting to what a sensor sees at that moment.
Many filings here combine sensing with judgment, like a lidar that adapts to fog or filters reflective glare, paired with models that decide what a lane change, an acceleration, or a stranger's next move should look like, rather than covering hardware alone.
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