LiDAR Simulation Patents: Who Leads, Where the Gaps Are 2026
- Flat, not rising. filings peaked at 4 in 2022 and the curve has not exceeded that since — this is a maturing niche, not a growth wave.
- US-centred filing. 8 of the tracked records route through the United States versus 4 in China, with Canada and South Korea each holding a single filing.
- Citation weight sits with two families. the two most-cited records outpace the rest of the set by a wide margin, concentrating influence rather than spreading it.
What this landscape covers
This dataset tracks patent families at the intersection of LiDAR sensing and computational simulation — ray tracing engines, point cloud modeling, signal processing models and sensor-fidelity simulation used to test or replace physical LiDAR hardware. The search combines LiDAR-specific title/abstract terms with IPC classes covering radar/positioning (G01S), digital data processing (G06F) and 3D scanning-specific sensing (G01S17/89), so it captures simulation claims tied specifically to LiDAR rather than generic sensor modeling.
Fourteen families is a small, specialist corpus. That size matters for how the numbers should be read: a single new filer can shift the yearly count, and the absence of a runaway leader says as much about the field's structure as any ranking would.
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Filing trend and technology mix
The filing curve and the IPC spread together show a field that grew in short bursts around vehicle autonomy programs rather than as a steady technology wave.
A flat curve since the 2022 peak
Filings moved from 2 in 2017 to a peak of 4 in 2022, and 2026's count of 1 reflects a partial year rather than a genuine drop-off — publication typically lags filing by around 18 months, so the most recent one to two years will always look thinner than they eventually turn out to be. Taken as a whole, though, the midpoint sits at the same level as the peak, which points to a plateau rather than acceleration.
Radar and positioning claims dominate, image and AI classes trail
G01S (radar, sonar and positioning) appears in 12 of the 14 records, confirming that most claims are anchored in core ranging and detection rather than pure software modeling. G06F (digital data processing) and G06T (image data processing) trail well behind at 4 and 3 respectively, and AI-specific classification under G06N appears in only 2 — a sign that machine-learning-driven simulation approaches are present but not yet the dominant claim strategy in this set.
Shares are the percentage of the 14 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on LiDAR System Simulation and Modeling with Eureka
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Try EurekaThe records carrying the most citation weight
Retrofit LIDAR-based vehicle system to operate with vision-based sensor data
Systems and methods for retrofitting a light detection and ranging (LIDAR)-based vehicle computing system to operate with vision-based sensor data are provided. A method implemented by a vehicle may include receiving, from sensors of a first sensing modality, sensor data associated with a surrounding environment; and retrofitting a vehicle controller based on a second sensing modality to operate on that data, where the retrofitting includes generating second-modality sensor data from the first-modality data and determining downstream outputs from it.Filed by GM Cruise Holdings, published 2023-12-07 — illustrates cross-modality retrofit claims rather than pure simulation, a distinct claim strategy from the ray-tracing and point-cloud families that dominate citation counts.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20210018599A1 | Three-dimensional scanning lidar based on one-dimensional optical phased arrays | 42 |
| 2 | US20190302141A1 | Optical air data systems and methods | 26 |
| 3 | CN108732587A | 一种基于扫描点云测距、测角的定权方法 | 5 |
| 4 | CN112530022A | 在虚拟环境中计算机实现模拟LIDAR传感器的方法 | 3 |
| 5 | US11300584B2 | Optical air data systems and methods | 3 |
| 6 | US12372651B2 | Retrofit light detection and ranging (LIDAR)-based vehicle system to operate with vision-based sensor data | 2 |
| 7 | US20230393280A1 | Retrofit light detection and ranging (LIDAR)-based vehicle system to operate with vision-based sensor data | 2 |
| 8 | CN116182810A | 一种机场道面工程数字化测绘系统及方法 | 1 |
| 9 | US12025741B2 | Three-dimensional scanning LiDAR based on one-dimensional optical phased arrays | 1 |
Citation counts reflect influence within the searched corpus and skew toward older publications; treat them as a signal of prior visibility, not of current commercial relevance.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Publication numbers are shown where the record carries one (9 of 9 rows); clicking a row searches Eureka by that number.
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With only 14 families, patterns are read from concentration and IPC placement rather than from statistical trend lines.
Two families carry most of the citation weight
US20210018599A1, on one-dimensional optical phased array scanning, and US20190302141A1, on optical air data systems, are cited well beyond the rest of the set. Both are older filings, consistent with the general rule that citation counts favour records that have simply had more time to accumulate references.
Simulation claims are filed as sensing claims
The overwhelming majority of records sit in G01S (radar, sonar and positioning) rather than in a software-only classification, meaning most applicants are framing LiDAR simulation as an extension of the sensing hardware claim rather than as a standalone modeling method.
US and China account for nearly all activity
Of the 14 tracked families, eight route through the United States and four through China, with Canada and South Korea contributing one each. That leaves the field with almost no filing presence in Europe or Japan within this dataset, which is worth checking against a broader search before ruling those jurisdictions out.
No single assignee shows sustained recent momentum
Among the tracked assignees, only one shows any activity in the most recent year, and it is a single filing. The rest — including GM Cruise Holdings, Shanghai Jiao Tong University, Ophir Corp, Ford Global Technologies and Yonsei University's industry-academic arm — show zero in the latest year, consistent with a field in a filing lull rather than active build-out.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to lidar system simulation and modeling, with the prior art for and against each one.
Who is filing, and where the claim space is thin
No assignee in this 14-family set holds a commanding share; filings are distributed across automotive, aerospace and academic entities, with recent-year activity scarce across the board.
Cross-modality retrofit claims from autonomous vehicle programs
GM Cruise Holdings and Ford Global Technologies both appear in the set with vehicle-platform-oriented filings, framing LiDAR simulation and sensor substitution as part of a broader autonomy stack rather than as a standalone modeling product.
Universities hold a meaningful share of the corpus
Shanghai Jiao Tong University, Beijing Forestry University and Yonsei University's industry-academic cooperation arm all appear among the tracked assignees, pointing to active academic research into point cloud and ranging methods alongside corporate filers.
A niche aerospace optical-sensing angle
Ophir Corp's optical air data systems filings sit adjacent to core automotive LiDAR simulation, applying similar ranging and signal-processing principles to aircraft air-data sensing rather than ground-vehicle autonomy.
| Assignee | Recent year | YoY |
|---|---|---|
| Beijing Forestry University | 1 | — |
| GM Cruise Holdings LLC | 0 | — |
| Shanghai Jiao Tong University | 0 | — |
| OPHIR CORP | 0 | — |
| Ford Global Technologies, LLC | 0 | — |
| Yonsei University Industry-Academic Cooperation Foundation | 0 | — |
| BASF Coatings GmbH | 0 | — |
| Shandong Siwei Zhuoshi Information Technology Co., Ltd. | 0 | — |
Where to take this analysis
A 14-family landscape is a starting map, not a final freedom-to-operate opinion. These are the natural next moves for an R&D or IP team working from this dataset.
Widen the classification search
This set is anchored to G01S, G06F and G01S17/89. Broadening into adjacent simulation-software classes could surface additional filers not captured here, especially outside the US and China.
Explore adjacent IPC classesTrack the two highest-cited families closely
US20210018599A1 and US20190302141A1 carry disproportionate citation weight. Any new filing strategy in optical phased-array scanning or air-data sensing should be checked against both before drafting claims.
Review citation chainsRe-run the trend after the next publication cycle
Because publication lags filing by roughly 18 months, 2025 and 2026 counts will rise as pending applications publish. A re-run in the next filing cycle will clarify whether the 2022 peak was a high point or a plateau.
Set a re-run reminderCommon questions on LiDAR simulation patents
This landscape tracks 14 patent families published between 2015 and mid-2026 that combine LiDAR-specific terminology with simulation-relevant IPC classes such as G01S, G06F and G01S17/89. That is a small, specialist corpus rather than a broad field, so individual filings carry more weight in the ranking than they would in a larger dataset. Because publication lags filing by roughly 18 months, the true count for the most recent one to two years will be higher once pending applications publish.
The dataset shows no single dominant assignee; activity is spread across automotive players like GM Cruise Holdings and Ford Global Technologies, aerospace-focused Ophir Corp, and academic institutions including Shanghai Jiao Tong University and Yonsei University's industry-academic cooperation arm. Citation weight, however, concentrates in two older US filings covering optical phased-array scanning and optical air data systems. A newcomer should check both the assignee ranking and the citation table separately, since they tell different stories.
Filing activity peaked at 4 families in 2022 and has not exceeded that level since, with the 2026 count of 1 reflecting a partial year of publication rather than a real drop. Comparing the midpoint year to the peak year shows a flat pattern rather than acceleration, which is consistent with a field that saw a burst of activity tied to autonomous vehicle programs rather than sustained year-on-year growth. Anyone tracking this space should re-check the curve after the next publication cycle before concluding the field has cooled permanently.
The United States accounts for 8 of the 14 tracked records, with China contributing 4, and Canada and South Korea each holding a single filing. That leaves the dataset with essentially no visible activity in Europe or Japan, though this may reflect the specific search terms and IPC scope used rather than the true global picture. Teams evaluating international freedom to operate should run a supplementary search focused on EPO and JPO filings before ruling those markets out.
Filing density concentrates heavily in core ranging and detection claims under G01S, while adjacent areas — multi-sensor fusion co-simulation, adverse-weather return modeling, synthetic point cloud labelling for training data, and signal processing validation frameworks — show comparatively thin coverage in this dataset. That does not guarantee those areas are unclaimed elsewhere, but it does suggest they are worth a targeted search before assuming the space is occupied. A first claim drafted around a specific validation methodology for signal processing models, rather than a generic sensing claim, may face less crowded prior art.
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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.