This storyline collects Waymo and Nvidia patents on the sensors, models, and software that let self-driving cars map roads, hear sirens, see through fog, predict hidden pedestrians, and time lane changes. Together the filings show two companies working to close the gap between raw sensor data and split-second driving decisions.
95 filings · tracking since Apr 2026 · latest May 2026 · updates automatically as new filings publish
US 2026/0131781 A1
A self-driving car that spots danger between other road users can plan safer routes around accidents that haven't happened yet, rather than just reacting after they occur.
US 2026/0194659 A1
Resolving depth ambiguity in time-of-flight sensors by distinguishing nearby objects from distant ones at the same signal phase, improving the reliability of lidar-style range measurements in crowded driving scenes.
US 2026/0194910 A1
Recording steering and braking sequences from one vehicle's passage lets the next car replay those moves automatically, cutting the need for live human operators to react to road hazards in real time.
US 2026/0196056 A1
Radio broadcasts from nearby vehicles get synthesized into overhead maps that show moving traffic beyond the car's direct line of sight, filling gaps that cameras and lidar struggle with in fog or around corners.
US 2026/0194616 A1
Radio frequency reflections can detect moving objects without cameras, using Doppler shift analysis across multiple signal bounces to identify velocity and position.
US 2026/0192823 A1
A highway exit might have a 'trucks only' sign that applies to one lane but not the three next to it. Getting that wrong at highway speed is a real problem, and Nvidia has filed a patent for a system that figures out exactly which lanes a sign is talking about.
US 2026/0194906 A1
The sensing race has centered on what cars detect, not how efficiently they process it. Qualcomm's grid system solves a real bottleneck: allocating compute only to regions where objects actually appear, rather than burning power on empty road sections.
US 2026/0194629 A1
The sensing race so far relies on dedicated hardware like lidar and radar. This filing shows wireless signals bouncing off programmable antennas can measure object velocity, potentially replacing specialized sensors with infrastructure already present.
US 2026/0197512 A1
Volumetric video capture from onboard cameras lets autonomous vehicles record roadside events in 3D rather than flat 2D, potentially giving downstream AI systems richer spatial data to judge what's newsworthy and worth uploading.
US 2026/0196060 A1
Compressing lane boundary data into minimal mathematical descriptions cuts the processing load when cars must track road geometry in real time across multiple sensor streams.
US 2026/0195983 A1
The race to map 3D space without lidar gains a competitor: Nvidia's neural network infers depth and object labels from 2D camera feeds alone, potentially cutting the sensor hardware that currently dominates autonomous vehicle cost.
US 2026/0192826 A1
Sensing systems trained on typical roads tend to miss anomalies, so Nvidia's approach weights rare scenarios during training to prevent the model from ignoring unexpected hazards when they do appear.
US 2026/0195922 A1
Sensor drift undermines mapping accuracy in fog or rain. Using celestial reference points lets cars recalibrate without ground infrastructure.
US 2026/0187807 A1
Tracking moving objects frame-by-frame requires fewer labeled examples when the model learns to predict motion patterns itself, cutting the human annotation burden for real-time 3D scene understanding.
US 2026/0185846 A1
A shared real-time map of nearby autonomous vehicles' positions and status cuts the information gap between pedestrians and self-driving cars, letting humans make crossing decisions with the same situational awareness the vehicles have.
US 2026/0184349 A1
Prediction in the storyline has moved from single-scenario planning to mapping behavior trees; this patent shows how to compute routes that work across all branches simultaneously rather than picking one path and adapting.
US 2026/0187138 A1
A neural network that maintains stable IDs for each moving object across video frames, so the system knows which car is which rather than treating each detection as a new thing.
US 2026/0187862 A1
The race to pinpoint lane positions moves beyond camera detection alone. Nvidia layers lidar readings onto camera detections to resolve the measurement gap cameras can't close on their own.
US 2026/0188021 A1
The race to see through fog and bad weather gets a boost here: by fusing camera data with pre-mapped locations of fixed objects, Qualcomm sidesteps the need for perfect sensor clarity on occluded signs and barriers.
US 2026/0189775 A1
A fisheye camera paired with infrared emitters in one module lets self-driving cars see clearly at night while keeping the sensor package small enough to fit existing vehicle designs.
US 2026/0186111 A1
A self-driving car needs to read both nearby bright objects and distant dark ones from the same laser pulse. Waymo's amplifier lets lidar adjust its sensitivity on the fly instead of losing detail to overexposure or underexposure.
US 2026/0187981 A1
A two-stage filtering system cuts the manual labor of finding training images from video footage. The first pass eliminates obvious negatives; the second ranks remaining candidates by relevance, so engineers label fewer, higher-value frames.
US 2026/0185687 A1
The sensing race includes not just better cameras but the manufacturing steps that keep them working. Waymo's patent covers quality control for infrared emitters, ensuring uneven light patterns don't blind its night-vision system before cars leave the factory.
US 2026/0184045 A1
The sensing race now includes keeping cameras optically clear in winter conditions. Tesla's conductive glass layer heats away fog and ice while preserving the image quality that computer vision needs to work.
US 2026/0186482 A1
Blocked sightlines at intersections need human judgment to resolve safely. Waymo's filing describes giving remote operators real-time sensor feeds to authorize incremental forward movement when the car's own perception hits a wall.
US 2026/0184348 A1
Simulating dangerous edge cases requires realistic traffic that's prohibitively expensive to create by hand. Zoox's system generates AI traffic automatically, letting engineers test self-driving responses to rare scenarios without waiting for real-world data.
US 2026/0187321 A1
Replaying recorded sensor data from real drives lets Waymo validate new software versions against thousands of actual road scenarios without returning to those locations, collapsing the test cycle from weeks of fresh driving to hours of simulation.
US 2026/0188114 A1
Sensing sirens through fog or urban canyons remains unsolved; Sony's approach inverts the problem by having emergency vehicles broadcast their routes directly to nearby navigation systems, creating alerts before audio detection would work.
US 2026/0189798 A1
Self-driving cars need camera protection from direct sunlight during highway driving. This filing shows how lidar can act as an early warning system, triggering camera adjustments before glare causes missed obstacles.
US 2026/0188022 A1
A two-stage filter first isolates traffic signs from road clutter, then classifies each one, reducing the computational load of scanning every roadside object for meaning.
US 2026/0187749 A1
Early frame processing lets prediction models start work on partial camera data rather than waiting for complete frames, shaving milliseconds off the perception pipeline.
US 2026/0175873 A1
A sampling-and-refinement loop lets the car generate thousands of steering options per second, score them against safety and efficiency metrics, and iteratively discard weak candidates to converge on the best path forward.
US 2026/0175875 A1
A robotaxi that brakes too hard wastes time; one that hesitates costs trust. This filing covers the gap between sensing a red light and executing the stop smoothly enough to feel safe.
US 2026/0175869 A1
Where the sensing race gets physical: Zoox moves beyond passive perception by patenting active repositioning to clear sensor sightlines when large vehicles block critical road features like traffic signals.
US 2026/0175835 A1
Predicting collision risks requires checking not just the planned path but alternative escape routes too. Zoox's filing shows how to evaluate both simultaneously, letting the car know its options before it needs to act.
US 2026/0179491 A1
Fleet-wide sensor sharing lets robotaxis know which curbs are actually available before arriving at a block, cutting the search time that makes parking a coordination bottleneck for autonomous vehicles.
US 2026/0181274 A1
A self-driving car needs to see clearly the instant light changes, say, exiting a garage into sun. This patent speeds up how cameras adjust exposure so the system gets usable images without the blind moment human eyes skip past instantly.
US 2026/0175834 A1
The sensing race now extends beyond what's ahead: Waymo's system monitors the truck itself, detecting tire failure and cargo shifts that human drivers feel but autonomous vehicles must measure through onboard sensors.
US 2026/0179389 A1
Detecting obstacles at vastly different distances requires cameras built for different focal ranges. Waymo's three-camera setup splits the job instead of asking one sensor to handle both nearby pedestrians and distant vehicles.
US 2026/0169124 A1
Continuous signal-strength monitoring lets self-driving cars detect when radar sensitivity degrades, preventing blind spots that faulty sensors might hide from the system.
US 2026/0170704 A1
Self-driving cars need to brake or swerve before danger arrives, so Waymo is training its AI to forecast how a traffic scene will evolve seconds ahead using live sensor feeds rather than reactive pattern matching.
US 2026/0170824 A1
The sensing race so far has centered on what data leaves the car. This filing shows Waymo cutting bandwidth by running recognition inside each camera instead of sending raw images to the main computer.
US 2026/0167232 A1
Merging route planning with traffic prediction into a single model eliminates the processing lag that comes from running these as separate steps, letting the car respond faster to other drivers' moves.
US 2026/0170954 A1
Coordinating autonomy levels across a fleet rather than having each vehicle decide independently lets cars share real-time sensor data to adapt faster to changing road conditions.
US 2026/0170852 A1
Camera-based lane topology parsing lets Tesla's system infer which roads lanes actually connect to, sidestepping the need for pre-loaded maps at fork points and merges where autonomous vehicles typically lose confidence.
US 2026/0167223 A1
Self-driving AI needs massive amounts of real-world driving data, but Zoox avoids deploying its own vehicles by mounting a full sensor suite on ordinary cars to collect training footage at scale.
US 2026/0167220 A1
The sensing race has focused on detecting objects; Zoox now pushes toward predicting their movement by converting raw sensor streams into directional flow maps that show speed and trajectory across the full scene at once.
US 2026/0162438 A1
Rare dangerous scenarios starve AI training data. Zoox's method synthetically generates edge cases that occur too infrequently in real driving to teach self-driving systems how to handle them safely.
US 2026/0159135 A1
The sensing race so far has focused on what's around the car; this filing shows how much prediction depends on reading intent before action. Waymo is betting that 3D body tracking gives its system seconds of warning that static obstacle detection cannot.
US 2026/0153357 A1
Sensor drift during operation poses a safety risk that existing calibration methods miss. Waymo's approach uses map data as a reference frame to detect and correct misalignment continuously, rather than relying on periodic manual checks.
US 2026/0156719 A1
A window combining resistive heating and thermoelectric cooling in adjacent zones lets Waymo control exactly where condensation forms, keeping critical sensor views clear without overheating the optical surface.
US 2026/0154935 A1
Self-driving cars need to know when weather degrades their sensors' reliability, not just when it degrades visibility. This filing lets them measure atmospheric conditions by cross-checking what cameras and lidar actually report.
US 2026/0153610 A1
As fleets grow denser, radar crosstalk becomes a real safety risk. Waymo's solution assigns each vehicle a unique signal code so radars can filter out interference from nearby cars.
US 2026/0154967 A1
A self-driving car in heavy fog could see beyond its sensor range by deploying a drone as a forward scout, turning poor visibility from a dead zone into navigable space.
US 2026/0152208 A1
Switching between time-based and distance-based planning lets self-driving cars handle both highway cruising and tight maneuvers without replanning from scratch each time.
US 2026/0154970 A1
Combining audio from onboard microphones with camera and lidar data lets Zoox detect emergency vehicles before they're visible, filling a gap where vision-only systems fail around corners or in heavy traffic.
US 2026/0145694 A1
Parallel redundant processors let each subsystem monitor independent sensor streams, so a hardware failure in one brain doesn't blind the car to what's happening around it during the limp-home sequence.
US 2026/0148535 A1
The sensor data already misses thin objects in point clouds. Zoox proposes fixing that during training by helping models weight sparse features equally with dense ones, filling a gap in how self-driving systems learn to see obstacles.
US 2026/0148404 A1
The sensing race needs depth perception to work reliably, and Nvidia cuts the bottleneck of manual labeling by automating both data generation and quality control in a closed loop.
US 2026/0147540 A1
Running neural networks on camera frames requires crushing massive matrix math fast enough for real-time decisions. Tesla's chip design processes thousands of multiply-accumulate operations simultaneously rather than sequentially.
US 2026/0148640 A1
Vehicles in the same fleet exchange sensor observations of each other's position to correct GPS drift and map errors without relying on infrastructure, improving localization accuracy during close encounters on city streets.
US 2026/0145710 A1
A self-driving car needs to know not just where pedestrians and cyclists are heading, but the precise timing of each movement. Zoox's approach trains separate models for trajectory and speed, letting each focus on its specific prediction problem.
US 2026/0145711 A1
Self-driving cars need to predict multiple pedestrians and vehicles simultaneously rather than sequentially. Waymo's neural network cuts the computational cost of running those parallel predictions in real time.
US 2026/0147121 A1
Self-driving systems need millions of labeled training examples to learn what they're seeing. Nvidia's approach lets the AI handle routine annotation work itself, shrinking the human bottleneck that currently slows down data preparation.
US 2026/0147122 A1
Waymo's approach extends LiDAR's effective range by harvesting ambient light data the sensor already collects, solving the blind spot problem where dark or distant objects fail to reflect active laser pulses back to the vehicle.
US 2026/0145703 A1
Depot routing adds a new layer to the sensing problem: vehicles must navigate and self-position within structured service environments, requiring real-time mapping of internal spaces and coordination between car and infrastructure.
US 2026/0141246 A1
Predicting road hazards requires the system to first translate sensor data into language, then use that description to anticipate what happens next.
US 2026/0141702 A1
Linking camera, lidar, and radar outputs to language descriptions lets the system learn which sensor details actually matter for driving decisions, rather than just matching sensor feeds to each other.
US 2026/0141732 A1
Distinguishing potholes from shadows requires stereo cameras to read depth shifts pixel-by-pixel, then feed those disparities into machine learning models that classify what actually threatens the vehicle versus what's just road texture.
US 2026/0140979 A1
Self-driving systems need to explain what they see in human terms so engineers can verify safe behavior. Waymo's neural network converts raw sensor streams into direct answers to specific questions, collapsing the gap between perception and reasoning.
US 2026/0141550 A1
Stereo depth estimates fail predictably in reflective surfaces, so Nvidia built an automated correction loop that catches and fixes bad readings without manual intervention, letting the system self-repair as it encounters problematic lighting or materials.
US 2026/0138647 A1
Tracking body geometry in real time lets the system spot imbalance before a cyclist or pedestrian actually falls, giving the car milliseconds to brake or swerve while the person is still upright.
US 2026/0140242 A1
Self-driving cars go blind when lidar bounces off reflective road signs and bike gear. Waymo's system pre-screens for these surfaces so it can dial down sensitivity before they saturate the sensor.
US 2026/0141254 A1
Self-driving cars trained only on flawless human driving freeze when reality deviates even slightly. Nvidia's approach creates a feedback loop where the AI learns recovery moves, not just ideal ones.
US 2026/0138621 A1
The sensing race requires filtering noise from raw camera data. This filing shows Waymo routing specific image regions to a language model so downstream systems get answers rather than raw pixels, reducing computational overhead.
US 2026/0138640 A1
The vision-only approach needs rare scenarios to train properly. Tesla's solution generates synthetic images of edge cases, blizzards, floods, traffic violations, rather than waiting for them to occur naturally on real roads.
US 2026/0138643 A1
Planning paths in real-time without recomputing from scratch requires pre-building options. Zoox's lattice approach lets the car swap between pre-computed trajectories when obstacles appear, rather than generating new routes on the fly.
US 2026/0141618 A1
The sensing race needs massive training datasets. Nvidia's approach sidesteps real-world data scarcity by synthetically generating multiple views and conditions from single images, multiplying training scenarios without new camera hardware.
US 2026/0140505 A1
Collecting rare driving scenarios randomly wastes miles; this filing lets Waymo's fleet converge on locations where edge cases actually occur, compressing training data into useful situations.
US 2026/0138641 A1
Continuous friction estimates let the car adjust braking and cornering limits as road conditions shift, moving from static safety margins to live performance boundaries.
US 2026/0141307 A1
Self-driving cars need grip estimates, not just rain alerts. This filing shows Waymo building real-time surface condition sensing that lets vehicles adjust braking and steering before hydroplaning becomes a problem.
US 2026/0141729 A1
The sensing race so far has focused on what's ahead. This filing shows Waymo is building confidence in reading brake signals specifically by using dual cameras to filter out false positives from dirt or glare.
US 2026/0141805 A1
Acoustic localization lets the car pinpoint which nearby vehicle is honking, then notifies the driver of the source and intent, solving the ambiguity of whether a horn blast is directed at your car or someone else's.
US 2026/0133317 A1
Matching speed data to distance data across multiple objects simultaneously lets the car track what's actually moving and where, rather than creating false threat scenarios from sensor confusion.
US 2026/0131789 A1
Predicting other drivers' behavior in real time, Waymo's system generates multiple possible lane-change paths and scores them based on forecasted movements of surrounding vehicles, turning a judgment call into a rankable set of options.
US 2026/0131807 A1
The sensing race so far has focused on what cars perceive outside. This filing shifts focus to reading the human inside, ensuring the vehicle won't move until it detects genuine intent to depart alongside safe conditions.
US 2026/0131823 A1
Filtering out impossible sensor noise keeps ground detection reliable when LiDAR bounces produce false readings that could confuse navigation systems.
US 2026/0131805 A1
The sensing race so far has focused on what cars can directly see. This filing moves the puzzle forward by training AI to reason about hidden pedestrians, inferring risk from occlusion rather than waiting for visual confirmation.
US 2026/0129360 A1
The sensing race has focused on cameras and lidar, but Waymo's microphone system adds an earlier warning layer: acoustic detection lets robotaxis react to sirens before any visual sensor picks up the vehicle, collapsing response time.
US 2026/0129148 A1
Detecting fog optically rather than guessing from degraded data lets the sensor adjust its own parameters in real time instead of relying on pre-set configurations to handle unknown conditions.
US 2026/0129150 A1
Projecting stereo depth onto a top-down map shrinks the compute burden by converting raw camera data into a format self-driving stacks can act on faster, letting embedded chips handle the geometry without offloading to a central processor.
US 2026/0129149 A1
Self-driving systems need overhead maps to plan routes, but generating them from stereo cameras usually demands expensive processors. Nvidia's approach compresses this conversion into a lightweight operation that runs on modest hardware.
US 2026/0118129 A1
Sensor data fusion into driving decisions requires real-time scene understanding. Waymo's transformer model collapses the pipeline from raw perception to steering commands into a single inference step.
US 2026/0120405 A1
Generating synthetic 3D road surface data sidesteps the months-long process of collecting and labeling real-world examples, letting Nvidia's self-driving systems train on road geometry variations without deploying test fleets.
US 2026/0118838 A1
Self-driving systems need to know how to recover from bad decisions mid-drive. Nvidia's approach creates synthetic recovery scenarios at scale rather than waiting for real-world near-misses to naturally occur.
Much of the filing activity here focuses on raw perception: how a self-driving car senses the world before it decides anything. Waymo's LiDAR system for fog and its microphone setup for hearing sirens both aim at filling gaps that cameras alone can't cover. Nvidia's patents on bird's eye view generation and synthetic data pipelines for road surfaces address a related problem: turning messy sensor input into something a driving model can actually use. Together these filings suggest both companies are still spending real engineering effort on the basic question of what the car can reliably detect.
A second cluster of patents deals with judgment calls once the car has good sensor data. Waymo's two-stage model for predicting hidden pedestrians and its system for forecasting crashes between other vehicles both push prediction further out, beyond what's directly visible. Its patent on smarter lane changes and Tesla's patent on reading intent before moving point to the same question: how confident does the car need to be before it acts? Nvidia's self-correcting training loop suggests part of the answer comes from letting the AI learn from its own mistakes in simulation rather than on the road.
Reading across the filings, the recurring theme is translation: raw signals becoming maps, sounds, or bird's eye views that a driving model can reason about. Readers should watch for more patents that connect perception to action, since that's where Waymo, Nvidia, and now Tesla appear to be converging. As always, a patent filing describes an idea a company wants to protect, not a feature confirmed for release.
The patents don't show a clear leader. Waymo's filings lean toward sensor fusion and prediction, like LiDAR that adapts to fog or models that guess where hidden pedestrians might be. Nvidia's filings focus more on turning raw camera and LiDAR data into usable formats, like bird's eye views and synthetic training data. Both are solving different pieces of the same puzzle.
No. A patent filing shows a company protecting an idea it wants to use, not a feature that has shipped. Some of these filings, like Waymo's siren-detecting microphone system or Nvidia's self-correcting training loop, describe research directions that may take years to reach an actual vehicle, if they do at all.
Two problems repeat often: making sense of messy real-world sensor data, and predicting what other road users will do before they do it. Fog, hidden pedestrians, sirens, and unpredictable drivers all point to the same underlying challenge, getting a car to react correctly to things it can't fully see yet.
Mostly this storyline tracks Waymo and Nvidia, but Tesla shows up too, with a patent on reading a situation's intent before the car moves. That overlap suggests the same underlying questions, like when a car should trust its own read of a scene, matter across the industry, not just to two companies.
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