Driverless cars represent a disruptive technological change in transportation as we know it. These vehicles are capable of sensing, navigating, and communicating with their external surroundings without any human intervention. They leverage various technologies including imaging, radar, laser optics, and GPS to navigate through dynamically changing road environments. In this report, we analyze the Intellectual Property (Patents) landscape of driverless car technology. Our analysis reveals key aspects relating to innovation in this technology, including filing trends, top assignees, their portfolio strength, and geographical coverage.

1. 1Driverless Cars: Patent Landscape Analysis

2. 2Driverless Cars: Patent Landscape Analysis
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3. 3Driverless Cars: Patent Landscape Analysis
Table of Contents
Executive Summary.......................................................................................................................................4
Introduction ..................................................................................................................................................5
Taxonomy....................................................................................................................................................10
Filing Trends................................................................................................................................................11
Top Assignees..............................................................................................................................................12
Patent Strength...........................................................................................................................................13
LexScoreTM
...................................................................................................................................................15
Licensing Heat Map.....................................................................................................................................16
Geographical Coverage...............................................................................................................................17
Taxonomy Definitions.................................................................................................................................18
Products......................................................................................................................................................22

4. 4Driverless Cars: Patent Landscape Analysis
Executive Summary
Driverless cars represent a disruptive technological change in transportation as we know it. These
vehicles are capable of sensing, navigating, and communicating with their external surroundings without
any human intervention. They leverage various technologies including imaging, radar, laser optics, and
GPS to navigate through dynamically changing road environments.
Driverless cars can dramatically reduce the number of road accidents and traffic congestion, leading to
increasingly deeper research into technologies that enable them. It is expected that the usage of
driverless cars will reduce fuel consumption by 20% through adaptive propulsion systems, cloud
intelligence and improved driving dynamics. All these factors in addition to the “science fiction” which
surrounds the technology have made driverless cars one of the most talked about emerging
technologies of this decade. Driving is so ubiquitous to large populations that such a positive
revolutionary change in driving is indeed very attractive to the general public.
Governments around the world are also showing active interest in driverless car technologies. States in
the US, such as California and Florida, have already passed laws which approve the usage of
autonomous cars. Car makers and technology providers around the world are conducting extensive
research and development to accelerate the commercialization of driverless cars. Significant
technological efforts are being put in making commutes more safe and efficient and it won’t be long
before driverless vehicles become an integral part of our transport systems.
In subsequent sections of this report, we analyze the Intellectual Property (specifically, patents)
landscape of driverless car technology. We discover that the majority of IP generation has occurred in
Anti-Collision systems and Dedicated Short Range Communication technology. The top three companies
with the highest number of patents and patent applications are Toyota, Robert Bosch and Nissan.
Geographically, Japan has seen the maximum number of patent filings as the leading company Toyota is
based out of Tokyo. Japan is closely followed by USA, China, and the European Union.
Using LexInnova’s proprietary patent analytics tool, LexScore™, we identify Robert Bosch as the leader in
this technology domain with a high quality patent portfolio, high patent filing activity, and a longer
average remaining life of the patents/patent applications. Toyota has a large number of patent filings,
but the company but was found to have relatively lower average quality patents, and lesser average life
remaining on its patents/patent applications.

5. 5Driverless Cars: Patent Landscape Analysis
Introduction
While R&D on driverless cars has picked up in recent times, initial research on this technology can be
traced back all the way to the 1920s. During the 1920s Houdina Radio Control demonstrated a radio
controlled car in front of the New York public1
. The driverless car research reached its next milestone
with the exhibition of “Futurama” in 1939. Futurama depicted cars which could use embedded systems
in and under roadways to guide themselves2
.
During the 1960s Universities started to take up research on driverless car technology with The Ohio
State University and Stanford University being the pioneers. However all the research methodologies at
the time were focused on modifying roadways to guide driverless cars3
. The 1980s saw the change of
focus from modifying road systems to improving cars to be autonomous irrespective of road conditions.
Mercedes-Benz’s robotic van demonstrated autonomous runs in busy city streets during this period,
making driverless technology famous. This decade also saw Carnegie-Melon University concentrating on
autonomous car research which helped it in achieving the leadership status it enjoys now.
Fast forward to the 21st
Century, the competition to benefit from the commercialization of driverless car
technology has boosted the research and several prototypes are in pilot phase on the road. Ever since
the 1980s, car makers have been continually developing technologies for driverless cars. A major
catalyst in development has been the DARPA Grand Challenge introduced by the Department of Defense
in USA. Technology giants like Google, Baidu, and Apple have now stepped in to providing technology
support required for driverless cars. Google has successfully implemented its technology on six Toyota
Prius cars and an Audi TT4
car. Automotive giants like Toyota, Robert Bosch, and Nissan have also
dedicated significant resources and investment into research and pilot testing of driverless car
technology.
Amidst these developments, in May 2013, the National Highway Traffic Safety Administration (NHTSA)
has classified driverless cars into five levels based on the level of autonomous capabilities:
Level 0 - No Automation: Driver controls all the functions of the car. No Automation.
Level 1 - Function Specific Automation: One of the Control Systems is automated. For Example,
Automatic Braking or Stability control.
Level 2 - Combined Function Automation: At least two of the Control Mechanisms are automated in
unison. For Example Adaptive Cruise Control with Lane Keeping.
1
“Phantom Auto' will tour city”. The Milwaukee Sentinel. Google News Archive. 8 December 1926.
2
The Original Futurama. (n.d.). Retrieved from http://www.wired.com/2007/11/ff-futurama-original/
3
"This Automobile Doesn't Need Driver". Palm Beach Daily News. Google News Archive. 1966).
4
Exclusive: Google Expands Its Autonomous Fleet With Hybrid Lexus RX450h. (n.d.).Retrieved from
http://www.wired.com/2012/04/google-autonomous-lexus-rx450h/

6. 6Driverless Cars: Patent Landscape Analysis
Level 3 - Limited Self Driving Automation: All the systems are automated. The car can sense critical
situations in which it cedes the control to the driver.
Level 4: Full Self Driving Automation: All systems are fully automated requiring no human intervention.
Fig.1: Levels of Driving Automation [NHTSA]5
The current decade will prove transformational for driverless cars with the technology transitioning
from concepts and pilot cars to actual production models. Toyota’s aptly named Highway Teammate
along with Nissan’s Level 3 autonomous car are expected to be launched into commercial market by
20206
. Several other companies have geared up their research to provide fully autonomous Level 4 cars
to public as soon as possible, with technology giants like Google expected to lead the way.
There are many Level 1 and Level 2 cars in the market already. Some estimates put this number at 0.3
million worldwide. This number is estimated to skyrocket over the next 5 years with a CAGR of 134%
reaching an all level combined number of 10 Million autonomous cars worldwide7
by 2020.
5
Ministry of Transport « transportblog.co.nz. (n.d.). Retrieved from http://transportblog.co.nz/tag/ministry-of-transport/
6
Toyota’s ‘Highway Teammate’ is meant to help humans, not replace them. (2015). Retrieved from
http://www.digitaltrends.com/cars/toyota-highway-teammate-self-driving-car/
7
Greenough, J. (2015). 10 million self-driving cars will be on the road by 2020. Retrieved from
http://www.businessinsider.com/report-10-million-self-driving-cars-will-be-on-the-road-by-2020-2015-5-6?IR=T

7. 7Driverless Cars: Patent Landscape Analysis
Fig.2: Estimation of Driverless Car market share and its growth8
Driverless technology is expected to expand beyond personal cars to public sector transportation as
well. Local governments in many metropolitan cities are aiming towards automated transit systems to
de-congest their cities in an effective manner. The first buses of this kind will be introduced in
Switzerland in spring 20169
. The Chinese bus company, Yutong is running similar trials and is expected to
open services to public in near future. We are in the stage of level 2 cars and looking forward to a level 3
car in another half a decade’s time. Many believe that in another 50 years, we will see 100% penetration
of autonomous cars.
The graph below predicts the amount of sales by level of penetration of driverless cars into the
automotive industry. It is estimated that by 2070, every car being bought will have Driverless technology
installed as a primary requirement10
.
8
Greenough, J. (2015). 10 million self-driving cars will be on the road by 2020. Retrieved from
http://www.businessinsider.com/report-10-million-self-driving-cars-will-be-on-the-road-by-2020-2015-5-6?IR=T
9
The world’s first autonomous buses will debut in Switzerland in spring 2016. (2015). Retrieved from
http://www.digitaltrends.com/cars/first-autonomous-buses-debut-in-spring-2016/
10
Autonomous vehicle implementation predictions. (2015). Retrieved from http://www.vtpi.org/avip.pdf

8. 8Driverless Cars: Patent Landscape Analysis
Fig.3: Estimation of Driverless Car Market Share in future.11
Driverless cars are expected to not only boost the sales of automakers, but also of mapping platform,
technology OEMs and automotive suppliers. Automakers like Tesla, BMW, etc. are buying devices from
automotive suppliers like Robert Bosch and from mapping firms like TomTom12
to enable higher levels of
automation and precision. Internet Search giants like Alphabet (Google) and Baidu are converting cars of
major automakers into autonomous cars using the technology they have developed on their own.
The world’s largest automotive supplier by sales, Robert Bosch, collaborated with the second-largest
high-definition mapping company by sales, TomTom to ensure continuous flow of high definition
mapping data13
. TomTom’s maps are already being used in cars being tested by Bosch on highways in
the U.S and Germany. Bosch will use its engineering expertise to help make TomTom’s maps more
accurate and work seamlessly with data produced real time by the car using sensors. Universities have
stepped up as well to collaborate with various automakers and technology companies to accelerate the
progress in the field of driverless car research.
One of the leading research units in the field, Carnegie-Mellon University had announced a collaborative
research lab with General Motors back in 2008. Carnegie-Mellon has also collaborated with ride share
technology company, Uber to research on autonomous taxi Infrastructure which can run seamlessly in
major cities. Toyota has also identified the importance of universities and provided a combined funding
of $50 million to MIT and Stanford14
.
11
Autonomous vehicle implementation predictions. (2015). Retrieved from http://www.vtpi.org/avip.pdf
12
Who provides Google with its driverless car technologies? (n.d.). Retrieved from http://www.techworld.com/news/personal-
tech/driverless-car-tech-brings-bosch-big-bucks-3619431/
13
How Bosch and TomTom are capitalizing on the driverless car movement. (2015). Retrieved from
http://fortune.com/2015/07/22/bosch-tomtom-driverless-car/
14
Toyota Plans to Invest $50M in Driverless Car Research at Stanford, MIT. (n.d.). Retrieved from
http://www.claimsjournal.com/news/national/2015/09/08/265605.htm

9. 9Driverless Cars: Patent Landscape Analysis
In a similar vein, the Engineering and Physical Sciences Research Council (EPSRC) and Jaguar Land Rover
have jointly funded up to 11 million euros into the project which will also include five other fronts of the
technology on which 10 UK universities will be working15
.
At this key juncture of transformation from research to production, this report studies the intellectual
property of driverless car technology to identify innovators and potential leaders who will be positioned
to dominate in the near future.
15
University to contribute to £11 million driverless cars project. (n.d.). Retrieved from
http://www.southampton.ac.uk/news/2015/10/driverless-cars-epsrc-funding.page

10. 10Driverless Cars: Patent Landscape Analysis
Taxonomy
Driverless Car technology is disruptive and definitely the game changer for the automotive Industry. The
transformation which can be achieved with this technology is incomparable to the progress made in the
last 100 years of automotive research. Though the current cost of commercialization and the equipment
makes it non-viable in its current form, the enormous potential of this technology has attracted all kinds
of companies, from Alphabet and Baidu to automotive leaders like Toyota, Volkswagen and Bosch to
heavily invest in the race to launch the first autonomous cars on a consumer scale.
In our study, we have classified patents/patent applications according to the broad technologies
involved in driverless car technology, such as Control Mechanisms, Communication Systems (such as
radar/lidar systems) and various kinds of ancillary equipment needed for these technologies to perform.
Our research finds technologies such as Adaptive Cruise Control and Anti-Collision Systems have the
highest number of patents/patent applications filings, followed by Braking Control Mechanism and
Communication Systems. Media and Sonar Systems have the least number of patent filings with only
396 and 597 patents/patent applications respectively.
Table.1: Taxonomy

11. 11Driverless Cars: Patent Landscape Analysis
Filing Trends
The number of patents/patent application filings in the driverless car technology has constantly
increased from 1995 to 2008. The economic recession of 2008-09 affected majority of the automobile
makers and decreased their cash flows. This meant the manufacturers couldn’t allocate as much funds
as they otherwise would have to research and development. This forced them to slow down research on
driverless cars. This explains to fall in number of patents/patent applications in 2009.
The filing trend for driverless car technology has mostly been on the upward trend. The number of
patent/patent applications being filed has witnessed a dramatic rise since 2009 as reflected by the slope
of the graph. From 311 filings in the year 1996, the number has risen to a whopping 1,861 in the year
2013. The dip after 2013 is because many of the applications filed haven’t been published yet. It is safe
to assume that the technology is still continuing on its positive growth trend and the number of patent
filings for the year 2015 might cross the 2,500 mark.
Fig. 4: Patent Filing Trend in the field of Driverless Cars over the years
0
500
1000
1500
2000
2500
3000
NumberofPatents
Filing Year

12. 12Driverless Cars: Patent Landscape Analysis
Top Assignees
The figure below shows the number of patents/patent applications related to driverless cars. Based on
our research, Toyota, Robert Bosch and Nissan have the most patent filings with 3110, 2665, and 1169
filings respectively. Volkswagen-Porsche (1140 patents/patent filings) has strong portfolio in signaling
and collision responsive systems while Daimler (961 patents/patent filings) have strong portfolio in
Collision Responsive Systems, Collision Detection and Pedestrian Safety Systems. Mitsubishi (231
patents/patent filings) has concentrated on Signaling and Vehicle Steering Systems, while Panasonic
(220 patents/patent filings) has patents on Vehicle Steering and Passenger Safety Systems.
The list of top 20 assignees is dominated by automobile manufacturers like Toyota, Nissan, etc.
Automobile suppliers like Robert Bosch, Valeo SA and Mando Corporation also have a significant
number of patents in the areas of Signaling and Steering Systems. Alphabet holds 238 patents/patent
applications, majority of which are in V2V and V2I communications.
Fig. 5: Top Assignees with the diameter of the circle representing their Intellectual Property

13. 13Driverless Cars: Patent Landscape Analysis
Patent Strength
The patents in our report are ranked automatically by our proprietary tool that relies on an algorithm
developed by Mark A. Lemley, Kimberly A. Moore, John R. Allison, and R. Derek Trunkey in their research
paper, "Valuable Patents." Historical research has proven that 97% of the litigation-worthy patents in a
portfolio are found in the top bracket of patents ranked by using this algorithm.
The table below shows a break-up of high strength patents/patent applications in driverless car
technology, under various technology areas. The highest number of high strength patents filings, with
484 active patents/patent applications fall under the ambit of Collision Responsive Systems, but this
number corresponds to only 7.3% of the total filings under this technology area. Vehicular Safety
Systems have the lowest number of high strength patents, with only 25 patents/patent applications.
Digital Computing Systems have a relatively high share of high strength filings, with 15.28% of high
strength patents/patent filings while only 1.25% of patents/patent applications which fall under the
technology head Adaptive Braking systems have high strength.
Table 2: Taxonomy representing high strength patents.

14. 14Driverless Cars: Patent Landscape Analysis
The table also shows a break-up of high strength patents/patent applications in driverless car
technology, under various technology heads. Collision Responsive systems have highest number of high
strength patents, with 484 patents/patent applications followed by digital computing with 464 patents/
patent applications filings.
The figure below shows a break-up of high strength patents in the respective portfolios of top assignees.
While Toyota has the largest number of patents pertaining to driverless technology, Robert Bosch has
the largest number of high strength patents.
Fig. 6: Number of high strength patents of each company

15. 15Driverless Cars: Patent Landscape Analysis
LexScoreTM
We use LexInnova’s proprietary LexScoreTM
framework to identify driverless technology intellectual
property portfolio strengths and weaknesses. The figure below depicts the competitive positioning of
the top 20 assignees in this domain. The assignees are compared on the basis of quality score, average
lifetime and the number of patents in their portfolio.
We use our proprietary algorithm (based on bibliographic information and claim characteristics of an
invention) to calculate the quality of inventions. The diameter of the circles represents the relative
number of filings of patents/patent applications of each company. The circles that are present in the top
right region represent the assignees with portfolios which are exemplary in terms of both quality and
the average remaining lifetime. Ford is lying in the top right region, but its circle diameter is relatively
small as compared to that of General Motors which has more average life but slightly less patent
strength. Nissan is leading in terms of average strength of the portfolio, but lacks in average portfolio
lifespan. Hyundai’s portfolio has a high average lifespan, but the average patent strength is very less.
Toyota has the highest number of patents/patent filings followed by Robert Bosch.
Fig. 7: LexScore Analysis – Driverless Cars
Bosch, 2655
Toyota, 3110
Nissan, 1169
VolksWagen
(Porsche), 1140
Continental Ag, 1041
Daimler Ag, 961Honda, 952
General Motors, 907
Fuji Heavy, 350
Ford, 521
Hitachi, 446
BMW, 442
Hyundai, 643
Zeppelin Gmbh, 265
Valeo Sa, 413
8
13
18
23
28
33
7 8 9 10 11 12 13 14
AveragestrengthofPortfolio
Average life remaining of Portfolio

16. 16Driverless Cars: Patent Landscape Analysis
Licensing Heat Map
We use LexInnova’s Licensing Heat Map framework to identify sub-domains in the field of driverless car
technology where licensing activity is expected to be high. The size of the sections (representing
different technology domains) in the Heat Map indicates the number of patents/patent applications
filed in that domain. The size in other words represents the relative importance of each sub-domain,
while the color represents the likelihood of future licensing activity in that domain. We study the patent
holding patterns to color code the technology sub-domain for future licensing activity.
In this heat map, Red (and shades thereof) signifies a high chance of licensing activity in a certain sub-
domain, whereas Green (and shades thereof) represents a low chance of licensing activity in the sub-
domain. We follow 80-20 rule to decide the colors, where Yellow is assigned to the domains that lie on
the average median, i.e. 20% assignees having 80% of the patents/patent applications. The color drifts
towards shades of Red if 20% assignees possess less than 80% of the patents/patent applications, while
it drifts towards shades of green in the opposite case.
According to our analysis, Optical and Collision Detection systems are the sub domains which have the
highest possibility of licensing activity. Vehicle Speed Control systems, Collision Responsive and Adaptive
Braking systems are the sub domains which represent a relatively low chance of licensing.
Fig. 8: Heat Map representing most licensed areas of Driverless Car Technology

17. 17Driverless Cars: Patent Landscape Analysis
Geographical Coverage
The figure below represents the geographical filing trend of patents/patent applications related to
driverless car technology. Japan has seen the maximum number of patent filings in this technology
domain, closely followed by USA. China, Germany and South Korea have also seen a good number of
patent filings. Since the research of autonomous car technology is expensive and initially people with
high spending capacity are likely to be the target audience for it, only the developed nations and a few
developing nations have a good number of patent filings. Major car makers in the autonomous car
industry like Toyota being based out of Japan make it the leading country with 6492 Patents/Patent
Application. The US being the home country of tech companies like Alphabet and major car makers like
Ford and GM has 6047 Patents/Patent application under driverless car technology.
Germany also has a good number of intellectual property filed because of its massive auto industry and
being the home of major car makers like BMW and Volkswagen.
Fig. 9: Map representing jurisdiction with highest to lowest filing situations

18. 18Driverless Cars: Patent Landscape Analysis
Taxonomy Definitions
This table broadly discusses different segments into which the driverless car technology and intellectual
property has been classified. It also discusses various IPC classes which fall into each of those categories.
S.No Taxonomy Head Definition
1 Vehicle Propulsion Control Patents which include mechanisms that help control Engine
Propulsion and Transmission Control of autonomous cars have
been included in this class. This head has B60W, F02D and B60K
as major IPC classes.
2 Vehicle Speed Control Patents which discuss mechanisms that control speed of a
vehicle depending on the conditions of operation have been
included in this class. IPC Classes with high number of patents in
this head are B60K3100, B60W3016 and B60W1010.
3 Vehicle Steering Control Patents that include about mechanisms which control steering of
the vehicle during transit. This Control Mechanisms Act upon the
stimulators in environment around the car. B62D IPC Class talks
extensively about this.
4 Vehicle Stability Control Patents which discuss mechanisms to control Stability during
Vehicle cruise. Major IPC classes are B60W03002 and
B60G017015.
5 Others(ACC) This category is created to include a multitude of diverse IPC
classifications that could not be properly categorized into any of
the preceding technology heads. The patents with such
classifications constitute the Miscellaneous Others technology
domain.
6 Collision Detection Systems Patents which discuss about detecting impending collision of an
Autonomous car have been included in this subhead. Major IPC
classes are B60W3008 and B60Q00152.
7 Collision Responsive
Systems
Patents which discuss mechanisms to control a vehicle in the
face of an impending collision have been included in this
subhead. Major IPC Classes are B60T007220 and B60R0210134.
8 Adaptive Braking This category is created to Segregate Patents and Patent Classes
which talk about mechanisms which asses the environment

19. 19Driverless Cars: Patent Landscape Analysis
conditions around the vehicle during cruise and apply braking
mechanisms accordingly. B60T IPC Class talk extensively about
this and hence has been included in this class.
9 Automatic Braking This subcategory has been created to separate patents which
talk about automatic braking from the class of adaptive braking.
This Subhead includes patents and patent mechanisms which
talk about braking mechanisms which get activated at a certain
predefined stimulus in the environment around.
10 Cruise Assist Patents of this subhead are particularly applicable to Level 1 and
Level 2 Autonomous cars. This Subhead includes classes which
talk about driver assist systems during vehicle cruise. For
Example, Braking Assist, Steering Assist etc.
11 Parking Assist Patents of this subcategory particularly are applicable to Level 1
and Level 2 Autonomous cars. This Subhead includes classes
which talk about mechanisms which assist drivers during parking
maneuvers.
12 Warning/Alarm Systems Patents which talk about warning and alarm systems, Audio and
Visual, which assist drivers. For example, Drowsiness Warning,
Hand-free warning.
13 Lane Keeping Patents which discuss mechanisms which control vehicles in
cruise to switch lanes or to maneuver through lanes during
traffic have been included in this category. Major IPC classes
included in this are head are G05D001020 and B60W03012.
14 Digital Computing Patents which talk about systems to process (Coagulate and
Calculate) information from various parameters and feed it to
the Cruise mechanisms have been included in this subhead.
Major IPC classes included in this subhead are G01C and G06F.
15 Image Processing Systems This head is created to segregate patents which talk about
Environment/Image acquisition and the follow-up processing
systems from the above mention computing systems. Major IPC
classes are G06T and G06K00900.
16 Signaling Systems Patents and patent classes which discuss about equipment used
in signaling the driver or the environment around, the
information and statistics of vehicle cruise have been included in
this subhead. IPC Class B60Q talks extensively about this and

20. 20Driverless Cars: Patent Landscape Analysis
hence have been included.
17 Optical Instruments Patents which discuss optical instruments like Camera and Studio
Setup which gather visual information from surroundings and
send it to the cruise mechanisms to assist in vehicle cruise have
been included in this subcategory. Major Classes include
B60Q00144 and B60R01104.
18 Passenger Safety
Equipment
All the patents which discuss infrastructure that can ensure the
safety of passengers travelling inside a driverless car have been
included in this subcategory. IPC class B60R02 and its family
extensively talk about this and hence have been included in this
subhead.
19 Pedestrian Safety
Equipment
Patents which discuss devices which prevent Pedestrian Collision
and Vehicle maneuvers which follow up have been discussed
under this subcategory. B60T007040 is major IPC class that fall
into this subhead.
20 Vehicular Safety Patents which discuss about Safety systems in an autonomous
car like Secure command systems, Anti-Malware and Anti-theft
systems have been in included in this class.
21 Propulsion Equipment Patents which discuss infrastructure which enable propulsion
and transmission particular to autonomous cars have been
included in this subhead. B60K006200 and B60K006547 are the
major classes which discuss these domains.
22 Radar/Lidar All the patents which concentrate on systems which use Radar or
Laser based Radar to send or receive information have been
classified into this subhead. G01S01* takes the highest
precedence in this class.
23 Sonar All the patents which concentrate on systems which use Sonar as
the primary medium for information gathering have been
classified into this subhead. G08G001 is the primary IPC class in
this subcategory.
24 V2VCommunication
Systems
Patents which talk about communication between vehicles using
Dedicated short range Communication Systems have been
classified into this subcategory. B60W030095 and B60R001120
are the major IPC classes.

21. 21Driverless Cars: Patent Landscape Analysis
25 V2I Communication
Systems
All the patents which talk about communication between host
vehicle and other parts of the environment around the host
vehicle using Dedicated short range communication systems
have been classified into this subhead. B62D1010 and
G08G109000 are the major classes in this subcategory.
26 Navigation Patents which discuss systems which transfer information
regarding navigation of the vehicle have been classified into this
subhead. For Example, GPS and Inertial Navigation Systems.
27 Media Patents which consist of communication systems like radio and
video sets which are particular to autonomous cars have been
classified into this subhead.
28 Wired Patents which discuss Wired communication systems that can be
installed in autonomous cars have been place under this
subcategory.
Table 3: Taxonomy Head Definitions

22. 22Driverless Cars: Patent Landscape Analysis
Products
Toyota’s Lexus GS Highway Teammate
The car is a modified Lexus GS equipped with autonomous driving technology. The car has already been
tested on Tokyo’s Shuto Expressway in a series of trials covering functions like merging onto or exiting
the highway, and maintaining or changing lanes. The driver can switch into automated mode only after
passing through a toll gate and entering an on-ramp16
. The car uses detailed maps and its own sensors
for orientation purpose to keep track of everything around it. Toyota has mentioned that the technology
is being tested and is expected to be production-ready by 2020.
Fig. 10: Toyota’s Lexus GS Highway Teammate17
Tesla Model S
Tesla Model S car is the king of the semi-autonomous car market today. The vehicle has proven to be
self-efficient. It does not ask the driver to take over frequently. It is equipped with automatic steering,
wheel and braking control. The vehicular Communications System in this car is also futuristic. 18
All the
Tesla Model S cars function through a network. They can share and access information processed by
other cars.
16
Toyota’s ‘Highway Teammate’ is meant to help humans, not replace them. (2015). Retrieved from
http://www.digitaltrends.com/cars/toyota-highway-teammate-self-driving-car
17
Nguyễn, T. N., & Trần, L. V. (2007). Giải thuật lai cho bài toán sắp hàng đa trình tự sinh học = Using Ga-sa hybrid algorithm for
multiplesequence alignment problem. JSTD Tạp Chí Phát Triển Khoa Học Và Công Nghệ, 10(4).
18
Tesla “Owns” Semi-Autonomous Car Market. (2016). Retrieved from http://cleantechnica.com/2016/02/08/tesla-owns-
semi-autonomous-car-market/

23. 23Driverless Cars: Patent Landscape Analysis
Fig. 11: Tesla Model S19
Google X Driverless Car
Google is currently test driving its Google X Driverless Car on U.S. roads20
. The car is has registered 1
million test miles on the roads of California and Texas. Testing fleet includes both modified Lexus SUVs
and new prototype vehicles that are designed from the ground up to be support full autonomous
driving. Recently U.S. vehicle safety regulators have said the artificial intelligence system piloting a self-
driving Google car could be considered the driver under federal law. This statement by U.S. vehicle
safety regulators has amplified the chances of seeing the driverless cars on the road by 2018.21
Fig. 11: Google Driverless Car22
19
Tesla's "insane" Model S car could eradicate taxis. (2014). Retrieved from http://www.dezeen.com/2014/10/14/tesla-model-
sd-electric-car-driverless-autopilot/
20
Google Self-Driving Car Project. (n.d.). Retrieved from https://www.google.com/selfdrivingcar
21
Exclusive: In boost to self-driving cars, U.S. tells Google computers can qualify as drivers. (2016). Retrieved from
http://www.reuters.com/article/us-alphabet-autos-selfdriving-exclusive-idUSKCN0VJ00H.
22
There's one big difference between Google and Tesla's self-driving car technology. (n.d.). Retrieved from
http://www.techinsider.io/difference-between-google-and-tesla-driverless-cars-2015-12.

24. 24Driverless Cars: Patent Landscape Analysis