Autonomous Vehicle Patents: Top Companies & Filing Trends 2026
- 20.2% concentration at the top. The five leading assignees hold 208 of 1,032 records in scope, a visible but not dominant concentration — most of the field sits with a long tail of smaller filers.
- Filings grew 16% from 2021 to 2024. Filings rose from 99 in 2021 to 115 in 2024, the last year the dataset treats as complete; 2022 remains the peak year so far at 182.
- Control and localization classes dominate. B60W (hybrid/joint vehicle control) and G05D (control of non-electric variables) each cover just under a third of all records, well ahead of perception-focused classes like G06V and G06T.
Filing growth compares 2021 (99 records) with 2024 (115) — 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 1,032 records in scope (CR5), not by the ranked leaders only.
What the autonomous vehicle patent record actually covers
The dataset spans 1,032 published records filed against search terms covering vehicle localization, trajectory planning, obstacle detection and sensor fusion — the core technical stack behind self-driving navigation. Filings run from 2015 through the 2026 cut-off, though publication lag means the most recent one to two years understate real filing activity. The record mix leans toward control and path-planning claims rather than pure perception, with B60W and G05D each touching roughly a third of the corpus.
Receiving-office data shows the United States and China as the two dominant jurisdictions, with the EPO, WIPO/PCT, Germany and the UK trailing well behind — a pattern consistent with a technology still being filed nationally before it is consolidated into broader PCT strategies.
Filing trend and technology composition
Two views of the same 1,032-record corpus: how filing activity has moved year over year, and which IPC subclasses carry the claim density.
Filing activity, 2017–2026
Annual filings climbed from 63 in 2017 to a peak of 182 in 2022, then eased to 115 by 2024 — still 16% above the 2021 level of 99. Treat 2025 and 2026 figures as provisional; publication typically lags filing by around 18 months, so the true count for those years will rise as more records publish.
Where claims concentrate by IPC subclass
B60W and G05D each appear in just under a third of all 1,032 records, reflecting heavy claim activity around vehicle control and non-electric control systems. G01C and G01S, covering navigation and positioning sensors, sit in the 17-23% range. AI-model and image-recognition classes (G06N, G06V, G06T) are present but cover a smaller share of the corpus, each under 13% — a gap worth reading alongside the white-space discussion below.
Shares are the percentage of the 1,032 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Autonomous Vehicles & Navigation Patent Landscape with Eureka
This page is one run against one query. Ask Eureka your own question about autonomous vehicles & navigation patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaA representative trajectory-planning patent
Vehicle trajectory planning (US20240282121A1)
A system for vehicle trajectory planning. The system may include a polarized camera system configured to generate polarized images of a roadway to be driven over with a vehicle, a prior controller configured to generate a prior for the roadway based at least in part on the polarized images, and a driving assistance system configured to provide a driving assistance according to the prior.Filed by GM Global Technology Operations LLC, published 2024-08-22.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190064835A1 | Vehicle guidance systems and associated methods of use at logistics yards and other locations | 168 |
| 2 | US20190033085A1 | Neural Networks for Vehicle Trajectory Planning | 147 |
| 3 | US20200234582A1 | Integrative system and methods to apply predictive dynamic city-traffic load balancing and perdictive parking… | 131 |
| 4 | US9739881B1 | Low cost 3D radar imaging and 3D association method from low count linear arrays for all weather autonomous v… | 130 |
| 5 | US20190050648A1 | Object localization within a semantic domain | 124 |
| 6 | US20160299507A1 | Surface vehicle trajectory planning systems, devices, and methods | 124 |
| 7 | US20200174490A1 | Neural networks for vehicle trajectory planning | 122 |
| 8 | US20170270361A1 | Systems and methods for providing vehicle cognition | 114 |
| 9 | US20190034794A1 | Neural Networks for Vehicle Trajectory Planning | 112 |
| 10 | US20180005407A1 | Autonomous vehicle localization using passive image data | 100 |
Citation counts favour older records simply because they have had more time to accumulate citations within this searched corpus — read them as a signal of influence, not of current technical importance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the concentration and citation data signal
Three patterns stand out once the assignee ranking, filing trend and citation table are read together.
A visible top tier, not a chokehold
The five leading assignees account for 208 of the 1,032 records in scope — a real concentration, but one that leaves nearly 80% of filings spread across the rest of the ranked field. That mix rewards freedom-to-operate searches over blanket avoidance of a handful of names.
Growth held through 2024, not fading
Filings rose from 99 in 2021 to 115 in 2024, the most recent year the dataset treats as complete. The 2022 peak of 182 suggests a filing surge that has since normalised rather than reversed — the lower 2025-2026 counts reflect publication lag, not a slowdown.
Citation leaders skew toward early filings
The most-cited records in the corpus date to 2019 and earlier, including logistics-yard guidance and neural-network trajectory planning filings. That is expected in a searched corpus — older documents have had more years to accrue citations — so treat the citation table as a map of influence rather than of what matters most today.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to autonomous vehicles & navigation patent landscape, with the prior art for and against each one.
Who is filing, and what the co-filing pattern reveals
The leader in the ranking holds 62 records, with fifth place at 32 and tenth place at 17 — a steep drop-off that flattens into a long tail beyond the top ranks.
A clear single leader
The top-ranked assignee holds 62 records, roughly double the fifth-place count of 32 — a gap that marks it as the clearest single point of reference in the field, though not a majority holder.
Co-assignment is rare and specific
Only five co-assignee pairs appear in the dataset. The strongest pairing links two individual inventors at 34 shared records; the next strongest links Baidu USA with a related Baidu entity at 13 shared records, pointing to internal corporate-structure filings rather than joint ventures.
Steep early drop, long tail after
Filings fall from 62 at the top to 17 by tenth place, then spread thinly across the remaining ranked companies. That shape favours monitoring a short list of active filers while scanning the tail for emerging entrants rather than assuming the tail is inactive.
| Assignee | Recent year | YoY |
|---|---|---|
| Argo AI, LLC | 0 | — |
| NEWMAN DAVID E | 0 | — |
| MASSENGILL R KEMP | 0 | — |
| Ford Global Technologies, LLC | 0 | — |
| Uber Technologies, Inc. | 0 | — |
| Baidu USA LLC | 0 | — |
| TuSimple, Inc. | 0 | — |
| nuTonomy Inc. | 0 | — |
Where to take this analysis
The figures above describe the shape of the field; the next step is usually a targeted search against a specific claim or assignee.
Run a freedom-to-operate check
With concentration at 20.2% among the top five, most of the field's risk sits in the long tail. A targeted FTO search against specific claim language is more useful here than avoiding a short list of names.
Search this landscape in EurekaTrack the under-claimed branches
Sub-areas like cooperative multi-vehicle negotiation and predictive traffic-load balancing show thinner filing density than core control classes — worth monitoring before they fill in.
Set up alerts in EurekaVerify claim scope on citation leaders
The most-cited records date to 2019 and earlier. Confirm current claim status and family scope before treating them as blocking prior art.
Pull full family data in EurekaCommon questions about autonomous vehicle patents
In this dataset of 1,032 records, one assignee leads the ranking with 62 records, roughly double the fifth-place figure of 32. That gap makes the leader a clear reference point, but the top five together hold only 20.2% of all records in scope, so no single company controls the field. A freedom-to-operate review should still cover the long tail of smaller filers rather than stopping at the leading names.
Filings grew from 99 in 2021 to 115 in 2024, a 16% increase over that span, with 2022 marking the highest single year so far at 182 filings. Counts for 2025 and 2026 appear lower in the raw data, but that reflects publication lag of roughly 18 months rather than an actual slowdown — those years are still filling in. The safest reading is that filing activity has held steady at an elevated level since the 2022 peak rather than declined.
Vehicle control classes dominate: B60W (hybrid/joint vehicle control) and G05D (control of non-electric variables) each appear in just under a third of the 1,032 records. Navigation and positioning classes, G01C and G01S, follow at 23.4% and 16.7% respectively. AI-model and image-recognition classes such as G06N, G06V and G06T are present but each cover roughly one in ten records, a smaller share than the control-focused classes despite the attention perception systems get in public discussion.
Relative to the dense control and localization classes, several specific branches show thinner filing density: polarized-camera-based road priors, cooperative multi-vehicle path negotiation, predictive city-traffic load balancing, and fallback logic for sensor-fusion failure. These are not empty fields, but they carry noticeably less claim density than core trajectory-planning and control work, which makes them worth a closer novelty search before assuming the space is occupied.
The United States and China are the two dominant receiving offices in this dataset, with 397 and 366 records respectively. The EPO follows with 100, WIPO/PCT filings account for 74, and Germany and the UK trail at 23 and 11. That pattern suggests most applicants are still prioritising national filings in the two largest markets ahead of broader PCT or European consolidation.
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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.