Occupancy-Grid Sensor Fusion Patents: Top Companies & Trends 2026
Filing growth compares 2021 (2 records) with 2024 (6) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 38 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
Occupancy-grid sensor fusion sits at the intersection of probabilistic mapping and multi-sensor perception: it is the layer that turns radar, lidar and camera returns into a shared grid representation a planner can act on. This landscape draws on 38 published records spanning 2015 through the 2026 data cut-off, filtered to filings that explicitly combine occupancy-grid or autonomous-grid-mapping language with autonomous or self-driving vehicle claims. Patent families, not raw document counts, are the fairer unit here, and the assignee ranking below is built on that basis.
Filing activity peaked in 2019 at 10 records, and the most recent years in the trend are understated because publication typically lags filing by around 18 months. Reading the curve as declining because 2025 and 2026 show fewer records would be a mistake for that reason.
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Filing trend and technology composition
Two views of the same 38-record dataset: how filing activity has moved year over year, and which IPC subclasses carry the claim volume.
Filing trend
Filings rose from 2 in 2021 to 6 in 2024, a +200% increase over that three-year span. 2019 remains the single highest year on record at 10 filings; treat 2025 onward as still filling in given publication lag.
Technology composition by IPC subclass
G06N (AI-model computing) covers 39.5% of the 38 records and G01S (radar, sonar & positioning) covers 36.8%, ahead of B60W joint vehicle control at 23.7%. Because records carry multiple classes, these shares sum to well over 100% and should not be read against each other as a single pie.
Shares are the percentage of the 38 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Autonomous Driving — Occupancy-Grid Sensor Fusion Patent Landscape with Eureka
This page is one run against one query. Ask Eureka your own question about autonomous driving — occupancy-grid sensor fusion patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records in the field
EP3921777B1 — Determining a vehicle's driving context
Filed by Elektrobit Automotive and published 2026-03-11, this record illustrates how occupancy-grid claims are increasingly framed around driving-context determination rather than raw grid construction alone — tying sensor fusion output directly to downstream decision logic.Representative filing selected for its recency and claim structure, not for citation volume.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190266489A1 | Interaction-aware decision making | 116 |
| 2 | US20210131823A1 | Method for Vehicle Environment Mapping, Corresponding System, Vehicle and Computer Program Product | 70 |
| 3 | US20190113929A1 | Autonomous vehicle policy generation | 68 |
| 4 | WO2021175434A1 | System and method for predicting a map from an image | 32 |
| 5 | WO2019244060A1 | Method for vehicle environment mapping, corresponding system, vehicle and computer program product | 20 |
| 6 | US20210309264A1 | Human-robot collaboration | 17 |
| 7 | US20220129726A1 | Determination of the driving context of a vehicle | 12 |
| 8 | US20180231650A1 | Method and system for contextualized perception of physical bodies | 11 |
| 9 | US20180247216A1 | Method and system for perceiving physical bodies | 11 |
| 10 | US11093829B2 | Interaction-aware decision making | 8 |
Ranked by citation count within the searched corpus; older filings accumulate citations simply by being available longer, so treat this as a signal of influence rather than current relevance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers mean for a filing decision
The dataset points to a field where claim space around core grid-fusion mechanics is getting crowded, while several adjacent branches remain comparatively open.
The top of the field is settled
With the leader alone holding 7 records and the top five holding 25 of 38, new entrants are filing into a space where the core mechanics of grid fusion already carry dense prior art from a small set of automotive and semiconductor players.
Momentum is real but recent, not historic
The three-year climb from 2 to 6 filings sits well below the 2019 peak of 10, suggesting a second wave of filing activity rather than a continuous rise — likely tied to AI-model-based occupancy prediction rather than the earlier sensor-hardware wave.
AI computing has overtaken radar/positioning as the dominant class
G06N's presence in nearly four of every ten records signals that claims are increasingly written around learned occupancy prediction rather than sensor-level fusion logic alone, even as G01S positioning claims remain close behind.
Filing strategy still centres on the US
United States receipts (21) outnumber EPO (8), WIPO/PCT (5) and Germany (4) combined, which matters for freedom-to-operate checks: a clearance search limited to European offices would miss most of the record set.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to autonomous driving — occupancy-grid sensor fusion patent landscape, with the prior art for and against each one.
Where to take this research
The dataset points to specific next steps depending on whether the goal is filing, clearance or partnership scouting.
Run a freedom-to-operate check on the leader's claims
With one assignee holding 7 of 38 records, any new filing in core grid-fusion mechanics should be checked against that portfolio before drafting.
Explore assignee portfolios in EurekaTrack the 2024 filing wave by inventor
The +200% jump from 2021 to 2024 is concentrated in a short window; identifying which teams drove it can flag where the next filing cluster will appear.
Trace filing momentum in EurekaDraft around the under-claimed branches
Sub-areas like grid compression for V2X transfer and context-conditioned decay modelling show thinner density than the core fusion claims and may offer cleaner first-filing positions.
Map white space in EurekaFrequently asked questions
The ranking covers 23 companies, with the top filer holding 7 records and the top five together holding 25 of the 38 records in scope — 65.8% of the field. That group includes established automotive OEMs, a tier-1 supplier and a semiconductor firm, rather than a single dominant specialist. The next tier, from second through fifth place, files at a fairly similar pace, so the field has one clear leader and a competitive group behind it rather than a single runaway winner.
Filings rose from 2 in 2021 to 6 in 2024, a +200% increase, which is the clearest recent growth signal in the dataset. The historic peak was higher still, at 10 filings in 2019, so the field has already seen one filing wave before this recent uptick. Data for 2025 and 2026 should not be read as a slowdown, since publication typically lags filing by around 18 months and those years are still filling in.
G06N, covering AI-model-based computing, appears in 39.5% of the 38 records, narrowly ahead of G01S radar and positioning claims at 36.8%. B60W joint vehicle control, G05D non-electric variable control, and image and navigation-related classes each appear in smaller but still meaningful shares. Because a single record can carry several IPC classes, these figures overlap rather than summing to a whole field, which is expected for a cross-disciplinary area like this one.
EP3921777B1, filed by Elektrobit Automotive and published in March 2026, frames its claims around determining a vehicle's driving context rather than raw grid construction alone, tying occupancy-grid fusion output to downstream decision logic. That framing matters for anyone drafting nearby claims, since it occupies the specific link between fusion output and context determination rather than the underlying sensor-fusion mechanics themselves. Teams working on adjacent claims should check whether their approach ties grid output to context or decision logic in a similar way, since that is the narrower area this filing stakes out.
The core grid-fusion mechanics and AI-based occupancy prediction classes are the most heavily filed areas, given G06N and G01S each cover over a third of the 38 records. Branches such as dynamic grid resolution switching, cross-modal uncertainty calibration between sensor types, and grid compression for V2X transfer show thinner density by comparison. These are reasonable starting points for a first claim, though any drafting decision should be checked against the specific portfolios of the leading assignees rather than the aggregate class counts alone.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.