This watchlist tracks 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.
79 filings · tracking since Apr 2026 · latest Nov 2026 · updates weekly
based on all tracked filings in this watchlist · refreshes every week
These 79 filings from Google show Waymo working on how its self-driving cars see the world, make decisions, and handle the people around them.
The filings cluster around two areas: helping the car read other people correctly (spotting a falling pedestrian, checking if a turn signal matches actual movement, tracking people who disappear from view) and keeping rides safe and smooth for passengers (making sure the right rider gets in, guiding someone to the car with sound, avoiding hard braking from small sensor errors).
a dated entry each week this watchlist moves · older entries stay archived
This week's filings focus on how the car sees and reacts: spotting falling people, zooming in on brake lights, and recording why it made each choice. Safer turns and smarter stopping round out the batch.
This week's filings focus on the car's senses and how it talks to riders. One covers ignoring tiny false alarms from its sensors; the other uses headphone audio to guide people to the car.
This week's filings focus on how Waymo's car understands its surroundings and stays healthy. One covers smarter pickup spots; the other turns sensor cleaning into a way to check if the sensors still work.
This week's filings all push at the edges of what a robotaxi can sense and track. The car may soon remember hidden pedestrians, flag wrong riders, and label far-off still objects on its own.
This week's filings all focus on reading the road around the car: spotting turn signals, watching wheels, and tracking where people and vehicles are headed. Waymo is applying to patent more ways for its cars to understand what nearby drivers are about to do.
This week's filings focus on how Waymo's cars understand the world around them. New applications cover predicting what people nearby will do, reading signs in bad weather, and knowing what the car's cameras will see next.
This week's filings focus on how Waymo's cars read and react to the world around them. New ideas cover predicting other drivers' moves, avoiding ice, pulling over safely, and not blocking traffic.
This week's filings both deal with how the car handles information under pressure. One covers cutting unneeded sensor data; the other covers planning gear changes ahead of time.
This week's filing focuses on predicting crashes before they happen. Waymo is working on software that watches every nearby object and guesses where it will move next.
This week's new filing looks at using laser light bouncing off rain, fog, or snow to help a self-driving car figure out what the weather is doing around it. The focus is on helping the car's sensors read the road even when conditions are bad.
This week's filings focus on how Waymo's self-driving cars see and respond to the world around them. One filing covers predicting how quickly a human driver will react, while the other looks at using laser sensors to help cameras capture clearer images in the dark.
This week's filing focuses on how Waymo's self-driving cars handle spots the car's sensors cannot see, suggesting the team is working on ways to keep the car moving safely even when part of its view is blocked.
Google’s tracked filings by month · newest first
15 or more filings that month · 5 to 14 · under 5
the problems Google keeps filing on · each with its three newest filings · new filings join every week
A self-driving car that can't see is dangerous, but only if it doesn't know it can't see. These filings cover ways for the car's sensors to detect their own failures, from radar going quiet to cameras losing clarity to fog and frost blocking the view.
Pedestrians often step out from behind things with no warning, giving the car almost no time to react. These filings cover ways to predict where hidden people are, read body language, and spot cyclists about to fall.
Each sensor type has blind spots and weaknesses the other can cover. These filings address how to combine spinning light sensors with cameras, cut lag, improve long-range clarity, and stop sensors from interfering with each other.
Teaching a car to understand its surroundings the way a person would describe them is a hard problem. These filings cover systems that let the car answer questions about what it sees, predict what it will see next, and connect sensor data to plain language.
A self-driving car needs to guess what every nearby vehicle and person is about to do, not just react after it happens. These filings cover predicting crashes between other cars, planning routes while reading other drivers, and forecasting a few seconds ahead.
The car watches how a person's body moves to catch the first instant of a fall.
It checks which phones are physically inside the car to catch the wrong rider.
Sound alone triggers the response, before any camera spots the ambulance.
Your phone says one spot, the car knows better, and it corrects yours.
every tracked filing, month by month · counts are USPTO pre-grant publications, one per publication number (an application, not a granted patent)
US 2026/0351011 A1
Stopping at unmarked intersections requires the car to infer safe positioning from geometry and sightlines rather than road markings, extending perception beyond painted infrastructure.
US 2026/0351003 A1
A robotaxi could spot brake lights faster by having radar and lidar pre-screen camera data, cutting the computational load when split-second detection matters most.
US 2026/0349442 A1
A robotaxi that anticipates how neighboring vehicles cut corners during double left turns can position itself to stay visible and avoid last-second braking, smoothing the ride for passengers.
US 2026/0346875 A1
Extends the prediction angle beyond static hazards: the car now models human body geometry in real time to catch loss of balance before impact, shifting from reactive braking to preventive steering.
US 2026/0346855 A1
Replaying frozen sensor snapshots in seconds rather than simulating full scenarios from scratch lets engineers debug specific decisions like hard braking without waiting for lengthy reconstructions.
US 2026/0338571 A1
A robotaxi user could locate their car by sound alone without honking in a parking lot, using spatial audio keyed to head position and phone location data.
US 2026/0336668 A1
A smoother ride means the car won't panic when sensor noise makes nearby objects flicker slightly closer. Waymo's approach lets the car coast through predicted safety margins rather than hitting the brakes.
US 2026/0328918 A1
Detecting sensor degradation during routine cleaning cycles avoids gaps in perception that could slow the car or cause crashes. The patent embeds diagnostics into existing wash routines rather than requiring separate maintenance checks.
US 2026/0326793 A1
Cameras and lidar track actual foot traffic patterns on sidewalks to override map-based pickup locations, letting the car predict where pedestrians will emerge rather than relying on potentially outdated street layouts.
US 2026/0316793 A1
Extends the hidden-pedestrian work by adding memory: the car keeps a model of where pedestrians went when occlusion breaks the sensor feed, preventing false assumptions that they've simply disappeared.
US 2026/0320508 A1
Adds a passenger-identity check to the sensing arsenal: the car verifies who's inside by detecting nearby phones, catching mismatches before the ride starts.
US 2026/0320334 A1
Automating labels for distant objects cuts the manual annotation burden. By propagating lidar's close-range detections onto camera footage captured from far away, Waymo reduces the training data bottleneck that has slowed perception models.
US 2026/0309857 A1
Detecting sideways motion requires radar coverage from multiple angles. Waymo's dual-radar setup captures lateral movement that a single sensor misses, sharpening predictions of whether nearby vehicles or pedestrians will cross the car's path.
US 2026/0307683 A1
A robotaxi that reads steering angle gains early warning of what other vehicles will do next, cutting the reaction window down before movement actually starts.
US 2026/0307681 A1
Waymo adds a layer to its prediction work: reading what human drivers intend when an autonomous vehicle's lane position creates ambiguity about its own next move.
US 2026/0311495 A1
Detecting fake turn signals from glare and shadows requires cross-checking the blink pattern against actual steering wheel movement. Waymo's filing adds that verification step to filter out camera false positives from sun, shadows, and brake lights.
US 2026/0300725 A1
A unified prediction model across all sensors means the car can foresee obstacles and road users before they enter view, reducing reliance on reactive sensing alone.
US 2026/0299088 A1
Robotaxis need to read stop signs reliably whether it's raining or noon sun. This filing adds redundancy by having radar confirm what the camera sees, filtering out the glare and weather interference that plague single-sensor approaches.
US 2026/0296504 A1
Among the sensing and prediction patents here, this one moves past detecting what's nearby to forecasting multi-agent motion, predicting trajectories for all surrounding road users simultaneously to guide the robotaxi's own decisions.
US 2026/0296502 A1
A robotaxi that ranks its own moves against normal and worst-case traffic scenarios gains a built-in check on whether it's actually driving safely or just avoiding crashes by luck.
US 2026/0296497 A1
Using onboard sensors to correct a rider's GPS position before routing to pickup adds precision to the perception layer, solving the gap between phone location and actual curb placement.
US 2026/0296454 A1
Predicting road conditions seconds ahead requires fusing camera, radar, and lidar data into one model rather than processing each sensor separately, letting the car plan maneuvers before events fully materialize.
US 2026/0288169 A1
Autonomous cars could maintain safe speed and routing in winter conditions by measuring real-time grip loss rather than relying on weather data alone.
US 2026/0287373 A1
Detecting occluded parking spots by modeling what might be hidden behind obstacles lets the car plan gradual deceleration instead of sudden stops, extending the sensing timeline for safe pull-over maneuvers.
US 2026/0285374 A1
Modeling interdependent moves across all road users at once instead of predicting each actor in isolation, capturing how one person's action cascades into others' reactions.
US 2026/0285357 A1
Real-time detection of whether the vehicle itself is obstructing traffic flow, triggering automatic repositioning before gridlock forms. Extends the sensing watchlist from external hazards to the car's own footprint as a moving obstacle.
US 2026/0276831 A1
Filtering raw LIDAR output in real time lets Waymo reduce the computational load without sacrificing the detection of nearby hazards like pedestrians and cyclists that the watchlist tracks.
US 2026/0274273 A1
A car that shifts too late lurches and confuses passengers. Waymo's system predicts upcoming road geometry from sensor data to pre-stage gear changes, keeping acceleration and deceleration smooth enough to feel deliberate rather than reactive.
US 2026/0264723 A1
Extends the sensing work by adding prediction: the system forecasts trajectories for the vehicle and every nearby object, then checks for path intersections before they happen. Shifts collision detection from reactive to proactive.
US 2026/0259347 A1
Knowing road grip in real time means the car can brake appropriately before losing traction. This filing extends lidar beyond obstacle detection to surface conditions, filling a gap where maps and weather data can't measure what's actually under the wheels.
US 2026/0249885 A1
Estimating human reaction latency fills a gap in Waymo's existing sensing toolkit, which tracks object detection and motion prediction but not the cognitive delay between when a hazard appears and when surrounding road users actually respond to it.
US 2026/0253177 A1
Dual-exposure camera captures synchronized with lidar pulses to maintain image sharpness across sudden lighting transitions, directly sharpening the sensor fusion that glare filtering alone cannot solve.
US 2026/0243892 A1
A robotaxi in heavy rain needs to know which sensors have gone blind and by how much, then adjust speed and following distance accordingly. This patent maps each sensor's degradation in real time so the car can compensate before it gets into trouble.
US 2026/0238743 A1
Narrowing a sensor's field of view when weather degrades it keeps the system focused on nearby, readable space instead of trying to see farther through rain or fog where readings fail. This sharpens the car's ability to spot obstacles that matter most.
US 2026/0233755 A1
Waymo's filing lets cars pull live sensor feeds from nearby cars or roadside equipment to see past things blocking their view.
US 2026/0227521 A1
Waymo's filing watches for sensors getting worse over time, so the car knows when it can't trust what it sees.
US 2026/0217274 A1
The fleet-learning approach extends beyond what individual sensors see: cars can now inherit warnings about hidden pedestrians from previous trips through the same locations.
US 2026/0200501 A1
A robotaxi needs to spot the actual passenger among dozens of pedestrians and confirm the match before pulling up. This patent adds visual tracking of foot traffic patterns to solve that identification problem.
US 2026/0202585 A1
Waymo's filing uses coated optics to stop light loss before light enters the waveguide, improving the signal at the source.
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
Waymo's filing has lidar protect the 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 2026/0131781 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 2026/0131805 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.
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 watchlist 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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