Given the emergence of autonomous vehicles (AVs) and their expected impact on the global economy over the next decade, for over a year Tech+IP Advisory has been cataloging patents describing and claiming technologies relating to Level 4 and Level 5 (L4/L5) autonomous driving capabilities according to the definition published by the Society of Automotive Engineers.
The first two reports, published in March 2021 and July 2021, grouped identified patents into three distinct periods based on priority dates:
Pioneer Phase
R&D Phase
Productization Gen 1 Phase
Unlike most patent landscapes analyses, Tech+IP sought to provide transparency by publishing its search string queries and results. We hoped this openness would benefit the broader community and encourage experts and practitioners to offer feedback.
Tech+IP is pleased to report receiving substantial feedback from corporate patent attorneys and other practitioners, which has been incorporated that feedback into refined search and identification methodologies. This updated report includes all patents issued worldwide through December 31, 2021. The AV community indicated that while version 1 avoided “false positives”, some relevant technologies had been omitted. The updated search strings (detailed later in this document) and the maturation of patent applications into issued patents over the last several quarters have resulted in an enhanced dataset of approximately 72,000 active patents (roughly 46,000 patent families), more than double the version 1 results. We also conducted a more extensive backward citation analysis to better substantiate pioneering patents and their owners.
We welcome feedback and constructive criticism from interested parties. We believe an iterative process may yield the most complete and most useful AV L4/L5 Patent Landscape.
V2: Methodology and Discussion of Ehanced Queries
The Landscape was created through iterative searches using the Innography software platform (owned by Clarivate). Technical keywords were gathered from materials and market literature, with particular focus on terms associated with AV L4/L5 technology. These keywords were searched across the patent abstract, title, and claims, then combined with technology feature terms found in the full patent specification.
The search methodology consists of three components:
searching the abstract, title, and claim sections
searching the patent specification body
a process to remove false positives
The first two queries relate to the Boolean operator “AND”, indicating that results must be found in both. Keywords searched in the body of a patent help to refine and increase relevance in the results. While the full set of changes for v2 is detailed later in this document, we highlight and discuss some of the key enhancements:
A key concept of our query is the presence of the phrase “autonomous vehicle” or its synonyms (“self-driving vehicle”, “driverless vehicle”, etc.). The latest update consolidated duplicate search strings; instead of separate searches like “self-driv*” or “self driv*”, we amended this to a one search string concept – “self” NEAR/3 “driv*”.
Our v1 query methodology did not include searching the patent specification body, only the abstract, claims, and title sections. In v2, we added specification searches as well, but connected these searches with a Boolean “AND” operator.
Based on community feedback, we also incorporated additional subject matter that, while not limited to use in autonomous vehicles, is clearly important for them. For example, “lidar” and “radar” were added in queries of the abstract, claims, and title. We also expanded the use of the “NEAR” query concept to incorporate greater flexibility in word choice. For instance, we now query (“vehicle*” NEAR/5 ((“predict*” NEAR/3 “object*”))), and “collision avoidance” (from the earlier query set) has been replaced with “collision*” NEAR/3 “avoid*”. These enhancements added another 40,000 patents to the results and identified patents such as US patent 11,001,256 (Zoox, “Prediction and Avoidance for Vehicles”) and US 20210397185 A1 (Uatc, containing the abstract phrase “methods” for predicting object motion and controlling autonomous vehicles are provided”).
Some of the other new keyword concepts in the v2 query include: “time of flight”, “environment map”, “vehicle fleet”, “collision prediction”, “collision detection”, “object detection”, etc. Keywords added for v2 are indicated in green in the chart below.
Simultaneously, the Tech+IP team identified cases and keywords that yielded a large number of false positives and selected improved keyword terms to eliminate many of these while not increasing false negatives. These refinements are also shown in the chart below.
One ways in which feedback from readers of earlier versions of the Landscape was incorporated was through testing queries against known datasets of AV patents identified by their owners. This process has been useful in “training” the query set.
Finally, in this Landscape report, European Patent (EP) grants have been expanded to reflect their active nationalized states, providing a more accurate patent count per jurisdiction. For example, EP patent 2,625,079 (assigned to Waymo) has been nationalized in Germany, France, and Great Britain, and is counted separately for each country.
Keyword Search
V2 (Q1 2022)
(@(abstract,claims,title) (((“autonom*” OR =automated OR (“self” NEAR/3 “driv*”) OR “driverless” OR (“self” NEAR/3 “park*”) OR =automotive OR “autopilot”) NEAR/5 (“vehicle*” OR =truck OR =van OR =car OR =bus OR “automobil*”)) OR (((“autonom*” OR =automated OR “self” OR =automotive OR “autopilot”) NEAR/5 (=drive OR =driving OR =driven OR =mode OR “control*”)) AND (“vehicle*” OR =truck OR =van OR =car OR =bus OR “automobil*”)) OR ((“vehicle*” NEAR/5 ((( “collision*” OR “blind spot*”) NEAR/3 (“avoid*” OR “detect*”))OR =fleet OR “neural network”OR =lidar OR (“light detect*” NEAR/3 “rang*”) OR ( “predict*” NEAR/3 (“object*” OR “vehicle*” OR “pedestrian*” OR “person*” OR “position*” OR “behavior”)) OR (“detect*” NEAR/3 (“object*” OR “vehicle*” OR “pedestrian*” OR “person*” OR “position*” OR “behavior” OR “landing strip”)) OR ((“determin*” OR “identif*” OR “recogn*” OR “predefin*” OR “detect*” ) NEAR/3 (( “position*” NEAR/3 “vehicle*”) OR “traffic*” OR “destinat*” OR “speed*” OR “travel*” )) OR (“detect*” NEAR/3 (“driver*” OR “passang*”)) OR (“environment*” NEAR/3 “map*”)))))AND (@body (=lidar OR (“light detect*” NEAR/3 “rang*”) OR “radar” OR (“vehicle*” NEAR/5 “fleet”)OR (“time” NEAR/3 “flight”)OR =ToF OR ( “environment*” NEAR/5 “map*”) OR “environment* data” OR (“brak*” NEAR/5 ( “automat*” OR “autonom*” OR “control*”)) OR (“convolut*” NEAR/5 “network*”) OR (“learn*” NEAR/5 (“machine*” OR “deep”))OR (“virtual” NEAR/5 “object*”) OR(“artificial” NEAR/5 “intelligenc*”) OR ( “predict*” NEAR/5 (“object*” OR “vehicle*” OR “pedestrian*” OR “person*” OR “position*” OR “behavior”)) OR (“image” NEAR/3 (“recogn*” OR “detect*”)) OR “autonomy map*” OR (( “determin*” OR “identif*” OR “recogn*” OR “predefin*” OR “control*” OR “detect*” ) NEAR/5 ((“position*” NEAR/3 “vehicle*”)OR“traffic*” OR “destinat*”)) OR (“navigat*” NEAR/5 (“rout*” OR “path” OR “destinat*”)) OR “vehicle* to* vehicle* communicat*” OR “vehicle* to* everything* communicat*” OR “vehicle* to* infrastructure* communicat*” OR =V2V OR OR =V2X OR =V2I OR “cooperativ* adaptive cruise control*” OR (“detect*” NEAR/5 (“driver*” OR “passang*”)) OR ((“vehicle*” OR “plan*” OR “augment*”)NEAR/3 “traject*”)OR (“control*” NEAR/5 (“lane*” OR “traject*” OR “path”))OR ((“object*”OR “collision*” OR “blind spot*”)NEAR/3 (“avoid*” OR “detect*” OR “predict*”))))))
Keyword Search
V1 (Q3 2021)
@(abstract,claims,title) ((“autonomous” OR “self driv*” OR “self-driv*” OR “driverless” OR “automated” OR “fully automat*” OR “fully-automat*” OR “highly automat*” OR “highly-automat*” OR “autopilot” OR “self-park*” OR “self park*” OR “automat* move” OR “automat* moving” OR “automat* guid*” OR “autonomous operation mode”) NEAR/5 (“drive” OR “driving” OR =car OR “vehicle” OR “automobil*” OR “bus” OR “van” OR “truck”)) AND (“machine learning” OR “artificial intelligence*” OR “neural network*” OR “convolutional neural network*” OR “deep learn*” OR (“predict*” NEAR/5 (“object*” OR “vehicle” OR “pedestrian*” OR “position” OR “behavior”)) OR “object detect*” OR “collision avoidance” OR “avoid* blind spot*” OR “image recognition” OR “autonomy map*” OR “trajectory plan*” OR “detect* landing strip*” OR (“determin*” NEAR/5 (“position*” OR “speed*” OR “travel*” OR “object” OR “vehicle”)) OR “brak* control*” OR “environment* data” OR “plan* navigation route” OR “vehicle-to-vehicle communication” OR “vehicle to vehicle communication” OR =V2V OR “cooperativ* adaptive cruise control*” OR “vehicle to everything communication*” OR “vehicle-to-everything communication*” OR =v2x OR “vehicle-to-infrastructure communication” OR =V2I OR “vehicle to infrastructure communication”)
Removing False Positives
V2 (Q1 2022)
((@claims (("industrial" OR "warehouse*") NEAR/5 "vehicle*") OR ("medical" NEAR/5 "machine*") OR (“cloth*” NEAR/3 (“machin*” OR “vehicle”)) OR “clean* robot*” OR "marine" OR “unmanned ship” OR "locomotive*" OR ("emerg*" NEAR/3 "device") OR =elevator OR (“container” NEAR/3 “vehicle*”) "drone*" OR "aircraft*" OR "airplane*" OR "airship*" OR "aerospace*" OR "tractor*" "spacecraft" OR "driverless transport* system*" OR ("unmann* NEAR/3 “deliver*") OR ("automat* hinge") OR (("agricult*" OR "armour*") NEAR/5 "vehicle*") OR (("baggage*" OR "aerial" OR "underwater" OR "gatoreye" OR “unmanned aerial” OR "stowage" OR =rail OR "haulage*" OR “fly*”) NEAR/5 (=drive OR =driving OR =driven OR “vehicle*” OR“robot*”)))) OR (( @title (("industrial" OR "warehouse*") NEAR/5 "vehicle*") OR ("medical" NEAR/5 "machine*") OR (“cloth*” NEAR/3 (“machin*” OR “vehicle”)) OR “clean* robot*” OR "marine" OR “unmanned ship” OR "locomotive*" OR ("emerg*" NEAR/3 "device") OR =elevator OR (“container” NEAR/3 “vehicle*”) "drone*" OR "aircraft*" OR "airplane*" OR "airship*" OR "aerospace*" OR "tractor*" "spacecraft" OR "driverless transport* system*" OR ("unmann* NEAR/3 “deliver*") OR ("automat* hinge") OR (("agricult*" OR "armour*") NEAR/5 "vehicle*") OR (("baggage*" OR "aerial" OR "underwater" OR "gatoreye" OR “unmanned aerial” OR "stowage" OR =rail OR "haulage*" OR “fly*”) NEAR/5 (=drive OR =driving OR =driven OR “vehicle*” OR “robot*”))))
Removing False Positives
V1 (Q3 2021)
(@claims (“industrial”) OR (“warehouse*”) OR (“medical” NEAR/5 “machine*”) OR (“marine”) OR (“unmanned ship”) OR (“cleaner robot”) OR (“clothes machine”) OR (“emergency report* device”) OR (=elevator) OR (“patient*” NEAR/5 “hospital”) OR (“automat* baggage*”) OR ((“drone” OR “fly*” OR “aircraft” OR “underwater ” OR “tractor” OR “container” OR “unmanned delivery” OR “automat* hinge” OR “gatoreye” OR “driverless transport*” OR “spacecraft” OR “drone*” OR “unmanned aerial” OR “stowage” OR “robot*” OR “rail” OR “haulage*” ) NEAR/5 (“drive” OR “driving” OR =car OR “vehicle” OR “automobil*” OR “bus” OR “van” OR “truck”))) OR (@title (“industrial”) OR (“warehouse*”) OR (“medical” NEAR/5 “machine*”) OR (“marine”) OR (“unmanned ship”) OR (“cleaner robot”) OR (“clothes”) OR (“emergency report* device”) OR (=elevator) OR (“patient*” NEAR/5 “hospital”) OR (“automat* baggage*”) OR ((“drone” OR “fly*” OR “aircraft” OR “underwater ” OR “tractor” OR “container” OR “unmanned delivery” OR “automat* hinge” OR “gatoreye” OR “driverless transport system*” OR “spacecraft” OR “drone*” OR “unmanned aerial” OR “stowage” OR “robot*” OR “rail” OR “haulage*”) NEAR/5 (“drive” OR “driving” OR =car OR “vehicle” OR “automobil*” OR “bus” OR “van” OR “truck”)))
Landscape Results
Global Patent Activity
As of December 31, 2021, the AV L4/L5 Landscapes consist of approximately 72,000 patents worldwide (roughly 46,000 patent families). Note that only “active patents” are counted; abandoned, expired, or invalidated patents are excluded.
Figure 1 below shows the Landscape patents grouped by assigned company, specifically the top 20 companies. Collectively, these represent approximately 46% of all the Landscape patents. Geographically, the top 20 comprise a diverse mix: US and Europe companies account for 25% of the total patents (12% and 13% respectively), Chinese companies represent 3% (two companies ), and the largest concentration comes from Asia-Pacific countries (excluding China) at 18%.
Figure 2 presents a geographic breakdown of patents regardless of assignee type. Universities and research institutes comprise 6% of the total patents, with over 3/4ths of such patents are assigned to Chinese institutions, while only 4% of US patents are assigned to universities or research institutes.
Priority Data Analysis
A patent application’s priority date—the date it was first filed— is important because it typically reflects the timing of early R&D and can indicate importance in the marketplace depending on various circumstances.
Figure 3 organizes the Landscape by patent family priority date into three distinct periods: Pioneer, R&D, and Productization Gen 1 Phase. Reflecting the broad set of technologies applicable to the Landscape and the increased R&D investment in L4/L5 autonomous solutions, the data shows roughly exponential growth.
Over the next year or two, growth is expected to moderate somewhat, though the charts will be updated by Tech+IP to reflect filings from 2020 and 2021 that may not yet be published (by law, publication occurs no later than 18 months from the priority date).
Pioneer Phase (2010-2014)
The Pioneer phase comprises approximately 14,000 patents held by 1,600 different assignees. As of this update, six companies collectively hold nearly one-third (30%) of all Pioneer Phase priority dates: Toyota leads with 6%, followed by Bosch, Waymo, and Porsche at 5% each, and Denso and Ford at 4% each.
Overall, Japanese and other Asia-Pacific companies (excluding China) comprise 24% of all Pioneer Phase patents, followed by Europe (18%), the US (12%), and China (2%). Notably, universities and institutes hold only 3% of the identified Pioneer Phase patents. As expected, automotive Original Equipment Manufacturers (OEMs) and pure-play autonomous companies such as Waymo and Cruise are well represented.
R&D Phase (2015-2019)
As is often the case in broad-based R&D technology segments, this second phase of patenting reflects a period of rapidly increased patent activity. Nearly four times as many patents carry 2015–2019 priority dates as those from the Pioneer Phase patents, totaling approximately 50,000 patents. At the same time, the patent activity shows a large number of new entrants operating in the L4/L5 autonomous space. Approximately 45,000 unique companies held patents in this period, and 90% of the patents in the R&D phase were owned by “new entrants” (i.e., companies that did not hold patents with Pioneer Phase priority dates).
These new entrants include pure-play AV companies such Motional Ad, TuSimple, by Stradvision, Deepmap, Nio, Plus.ai, among others. A number of companies dramatically accelerated their patent holdings during this period, shifting their overall position in terms of the total number of AV patents. For example, Ford increased its patent activity nearly fivefold in the R&D Phase compared to the Pioneer Phase, while Baidu filings rose from 1463rd position to 8th. Early leaders such as Waymo and Denso saw their relative positions decline (Waymo, for example, dropping from 3rd to the 10th). In such scenarios, the impact of priority dates can become more important than total filings. The top 20 companies per number of patents in this phase are presented in Figure 5.
Productization Gen1 Phase (2020-Present)
This phase, encompassing patents with priority dates from 2020 to present, will be subject to substantial change over the next year as many filings reach their publication dates. As it currently stands, approximately 7,000 patents are owned by 1,900 different assignees (1,400 operating companies, 300 universities/institutes, 200 different inventors). Of the top 20 companies, 59% have patents filed in China while filing in the US currently represents only 15%, perhaps possibly reflecting non-US inventorship and patent prosecution strategies.
Backward Citation Analysis
Beyond cataloging identified patents, Tech+IP also conducted a comprehensive backward citation analysis, examining patents cited by identified patents in the landscape. When adjusted to exclude self-citations, this analysis helps to cast light on which patents may be the more important patents in the field, as reflected in “community recognition” through citation patterns. When adjusted to exclude self-citations, this analysis helps illuminate which patents may be most important to the field, as reflected in community recognition through citation patterns.
Tech+IP’s backward citation methodology is presented in Figure 7. In summary, the 72,000 patents comprising this Landscape cite approximately 182,000 unique patents as potential prior art, of which 9,000 are active patents relating to the AV L4/L5 technology space. These 9,000 patents are cited 48,179 times by other AV L4/L5 patents.
Table 1 shows the holders of patents most frequently cited by other patentees. For example, Waymo owns 417 patents that are cited in total 8,862 times by AV L4/L5 patents assigned to other companies. The citation ratio column normalizes the raw data by dividing the number of citations by the number of patents cited, helping to identify recognized and potentially important work. Among the top 20 companies by citation frequency, the State University System of Florida leads with the highest citation ratio (143.0), followed by Maplebear Company (49.4), Waymo (21.3), Allstate Corp (19.5), and Here Holding (18.3). Moreover, Verizon Communications, ranked 23rd by a number of citations made by others and not listed in Table 1, achieved a significant citation ratio of 22.0 (17 patents cited 374 times by others), indicating the importance of its AV portfolio.
An analysis of the 50 most-cited patents in the AV space reveals Waymo’s pioneering status: it holds 19 of 50 the most-cited patents, followed by Ford (4), Honda (3), and Intellectual Ventures, Cruise, and Zoox (2 patents each.)
The Top 5 the most-cited patents with technological categorization are presented in Table 2.
Final Thoughts
The rapidly growing and multifaceted nature of the L4/L5 Autonomous market is clearly reflected in the patents being published globally describing and claiming technologies with important uses in this space. This Tech+IP Landscape represents an effort to bridge the engineering and the patent world by identifying and classifying patents according to the definitions established by the Society of Automotive Engineers.
We recognize that creating such a landscape is complex and highly dependent on search methodology details. To make the process computationally tractable while minimizing false positives or false negatives, we have developed a baseline strategy that yields “adequate” (not perfect) results and is capable of iterative improvement. To drive continuous improvements and bring something useful to the patent community, we have opted for fully transparency in our search process. We encourage others to review, comment on, and challenge these findings. It was precisely this feedback from practitioners in the community that drove improvements from v1 to v2.
We fully anticipate, and hope for an AV Landscape v3 that is more complete and more useful. Ultimately, Tech+IP undertakes this work because we believe that when information is more readily available and assumptions are transparent, more deals get done, thereby improving the ecosystem for all participants.