Artificial Intelligence In The Automotive Industry - M&A Trend Analysis

AI IN THE AUTOMOTIVE INDUSTRY
M&A TREND ANALYSIS
OCTOBER 2019

CONTENTS
Key Trends Driving AI Adoption in the Automotive Industry.........4
The Need for Automotive Industry to Invest in AI.........10
Transformation in Business Models with the Introduction of AI.........11
Methodology for M&A Analysis.........17
AI-related M&A Across Automotive Industry Value Chain.........14
Acquirer Landscape........22
Technology Breakdown........24
Acquisition Trend #1: Computer Vision........26
Acquisition Trend #2: AI-based Data Analytics........36
Acquisition Trend #3: Conversational AI........46
Acquisition Trend #4: Cloud-based Services........50
Acquisition Trend #5: AI Hardware and Software........55
Acquisition Trend #6: Data Training........61
Acquisition Trend #7: Gesture Recognition........66
Acquisition Trend #8: Self-driving Software Stack........71
Acquisition Trend #9: Simulation Software........75
Acquisition Trend #10: Security........78
Acquisition Trend #11: Mapping Technologies........81
Acquisition Trend #12: Other Technology Trends........84
M&A Analysis of AI in Automotive: Technology Trends.........18
1.
3.
2.
4.
M&A Trend Analysis: Artificial Intelligence in the Automotive Industry 2

CONTENTS
Insights & Recommendations.........88
Acquisition Gap Analysis and Future Growth Opportunities.........89
Concluding Remarks.........95
References.........97
5.
6.

KEY TRENDS DRIVING AI ADOPTION
IN THE AUTOMOTIVE INDUSTRY

The automotive industry is currently undergoing dramatic changes with the rise of AI-driven intelligent systems.
AI technologies are unlocking new functionalities in the automotive space, driving exponential growth in the level
of automation, redefining of user experiences, and fueling the emergence of urban mobility standards. It is also
being incorporated in designing advanced driver-assistance systems (ADAS), which will eventually give rise to
fully autonomous vehicles with Level 5 autonomy.
The advancements in internet of things (IoT) and connected environments are further driving the trend for AI, with
connected cars now leveraging optimized connectivity solutions. Next generation 5G networks and hardware
innovations are adding to the momentum.

KEY DRIVERS
AI in Automotive
Reduced Latency
Near real-time processing and analysis for
mission-critical use cases
Detection and Classification
Object detection and classification is accelerating
roadmaps for self-driving cars
Analytics
Sensor fusion and data analytics with AI
solutions is helping in decision making
Increased Automation
With increasing levels of autonomy, reliance
on human drivers is reducing
Hybrid Computing Models
Deploying AI in hybrid cloud and edge
computing models for different workloads
Self-learning Capabilities
Advancements in AI techniques are giving rise
to self-learning systems
Enhanced User Experience
Digital assistants, infotainment, and integrated
AI cockpit are driving technology adoption
Digital Factory
AI-based tools are providing a standardized
and efficient manufacturing setup
THE DRIVERS OF AI TECHNOLOGIES IN THE AUTOMOTIVE INDUSTRY

The automotive industry is shifting from ADAS applications to the next
level of autonomy for self-driving cars. Autonomous vehicles as a trend is
strengthening – from personal cars and ride sharing, to public transport and
truck-based logistics. There is a growing consensus that autonomous vehicles
are the future of transportation.
Any discussion regarding advanced ADAS applications and autonomous
vehicles, however, is incomplete without including the level of AI integration
required. For every car manufacturer making inroads into the self-driving
space, many more software and technology companies are focusing on AI
solutions that cater to the different aspects of the implementation. Apart
from driving scenarios, these software and technology companies are
also developing solutions for mobility use cases, which are in dire need of
intelligent options.
AI-driven offerings are helping unlock new potentials in the existing vehicle
mobility solutions related to ride sharing, public transportation, and the
other connected vehicle use cases. Organizations are leveraging different
techniques to optimize allocation of vehicles to passengers in a setup
with robo-taxis, and are extrapolating the trend of autonomous vehicles
for effective autonomous fleet management. Furthermore, companies
focused on logistics and B2B (and B2C) product delivery-related services
are also investing in autonomous capabilities to gain an advantage from
intelligent mobility and delivery. Many of these mobility solutions are, however,
incomplete without a cloud backhaul supporting complex computation needs.
Cloud technologies are driving digital transformation in the automotive
sector with several use cases relying on cloud for connectivity, over the air
updates, route optimization, mobility management, analytics, and various
ADAS applications. Integration of AI in cloud is becoming a go-to strategy for
participants in the value chain to vertically integrate software solutions in a
self-driving car setup.

Autonomous vehicles are equipped with a multitude sensors, antennas and several
software packages, which facilitate the collection of in-vehicle data. Integration of
AI with cloud is simplifying analysis of heterogeneous data and accelerating growth
of predictive analytics and maintenance in the automotive sector. Participants
are increasingly becoming interested in predictive maintenance based on cloud
solutions – making it one of the key trends in the automotive space.
Predictive analytics is also being run on innovative platforms for delivering real
time results using edge computing solutions. Such platforms are introducing new
ways of monitoring vehicle health without relying on internet or cloud-based
computation. With this trend of hybrid architecture of computing resources, the
integration of AI technology is fast emerging as an imperative.
Apart from hybrid computing architectures, the demand for on the move
communication for connected vehicles is leading to the development of a hybrid
connectivity infrastructure where terrestrial and satellite networks can work in
tandem. Organizations are increasingly developing solution for vehicles that
can support multiple networks and seamlessly switch between them, potentially
eliminating downtime. AI solutions are helping in this smooth handoff between
the networks, with intelligent antenna technologies and beamforming techniques
increasing the efficiency of communication. Such hybrid solutions are typically
expected to stay functional round the clock for effective communication
requirements and an increase in automation.
IoT covers a wide variety of applications that need always on operation to ensure
optimum outcomes. With autonomous vehicles and connected cars forming an
integral part of the IoT ecosystem, the trend of always on systems in the automotive
industry is shaping up. New power management systems, no-load standby
conditions, and seamless connectivity solutions are complementing always on
functionalities for improving the overall self-driving car experience.

Another significant trend is the evolution of the automotive retail space. The evolution is
being supported by the increase in targeted marketing and advertisements, improved
customer experience in car ecommerce, development of centralized dealer hubs, an
end to end retailing experience with intelligent platforms that streamline customer
communication, and improved supply chain management. AI-based technologies are
instrumental in this direction, enabling real time response to customer requirements,
predicting demand through customer behavior analysis, and offering precise
personalization based on consumer preferences.
Key Trends Driving AI In Automotive
Autonomous
Vehicles
Mobility
Solutions
Cloud
Services
Hybrid
Connectivity
Always On
Operation
Car Retailing
Solutions

THE NEED FOR AUTOMOTIVE INDUSTRY
TO INVEST IN AI

TRANSFORMATION IN
BUSINESS MODELS
WITH INTRODUCTION OF AI
In order to incorporate AI algorithms across different products, solutions,
and services, the automotive industry needs to embrace an effective
approach. The different participants in the automotive value chain will
have to transform their traditional operating models and strengthen
capabilities for developing intelligent products and for creating value
through innovative technologies. Automotive companies need to redesign
their organizational strategies for identifying investment opportunities that
address technological gaps and help in adopting new business models.
Transformational models that can provide reduced operational cost, higher
revenue, customer-centric solutions, and lower risk can help create a
roadmap for the industry to implement potential AI-enabled use cases that
capture the different trends in the automotive sector.
Startups and disruptors are already trying to address the technical and
market challenges and develop effective business models that address it.
Incumbents, including automotive suppliers and manufacturers, need to
redefine business goals to sustain and thrive in the future transportation
market.
Mobility As A Service (MaaS)
MaaS-based models incorporate the different services related to ride-hailing, self-
driving, connected vehicles, fleet management, and electric vehicles. Disruptors like
Uber and Lyft have an upper hand in terms of monetizing their MaaS model, as they
are the early adopters. Traditional car rental providers are already reinforcing their
implementation techniques and are becoming a part of the mobility ecosystem.
OEMs, automakers, system integrators and solution providers are also aiming to
build new business models revolving around MaaS. Strategic collaboration and
investment can help OEMs and manufacturers to galvanize their experience in the
industry and address the market need for mobility services.
KEY BUSINESS MODELS
Data monetization
This model creates an opportunity for core automotive entities as well as for
companies adjacent to the automotive market, such as insurance, infrastructure
and other retailers, to capture new revenue streams by monetizing automotive
and related data. With the integration of AI technologies in the industry the
demand for data is increasing for adequate data training and structuring
purposes. Such requirements are allowing entities to generate revenue by selling
data related to different features including vehicle diagnostics, map usage,
mobility services, infotainment systems, and other in-car technologies and
background processes. Such datasets are perceived according to the customer’s
privacy at different levels of implementation. Additionally, the different entities
in the automotive value chain can also consider monetizing by selling data to
companies in different verticals like IoT and telecom.

Data from
Navigation Systems
Consumer
Behavior
Data
Vehicle
Diagnostics
Data
Vehicle
Sales Related
Data
Traffic Data
Related to
Weather
Data From
Cloud
Data from
Conversational AI
Connected car
environment generates
massive amount of data
under different categories
across the automotive
ecosystem. Leading to Data
Monetization Opportunities
Between Stakeholders
Fuel Monitoring
Data
Various Data Sources in an
Automotive Environment

Strategic
Routes For
Future Growth
Oppotunities
Acquisition
Buy AI companies for organic or inorganic growth
opportunities to unlock opportunities and build new business
models in the self-driving, mobility, connected car, and
vehicle retail market.
Collaborative Path
Consider partnership with startups, innovators, universities,
and R&D institutes to codevelop AI solutions, and products
for capturing new revenue streams and exploring new
automotive market segments.
Investment
Adopt the route to invest in AI startups, incubators, and R&D
firms developing technologies that are disruptive, innovative
or are addressing the challenges in the automotive sector
that could lead to financial benefits.Investment in AI technologies is vital for building
new business models and for driving future product
development. The different paths for growth
opportunities in AI automotive domain are investment,
collaborations and acquistions.
Companies need to consider collaborations and
global alliances for new technological skills that
drive AI solution development. Investment in relevant
startups is another approach to gain access to new
markets. Further, investment in incubation programs
can provide the opportunity for university spinouts in
the future. Acquisition of companies providing key AI
capabilities would also allow businesses to galvanize
new techniques and cutting-edge technologies in their
offerings with a shorter time-to-market, helping meet
business goals.

ACROSS AUTOMOTIVE INDUSTRY
VALUE CHAIN
AI-RELATED M&A

CONVERSATIONAL AI
SECURITY
AI-BASED ANALYTICS
GESTURE RECOGNITION
AI-BASED ANALYTICS
CONVERSATIONAL AI
COMPUTER VISION
CLOUD
*Color of the bars signify the activity across the value chain in the acquisition trend
Manufacturing & Operations Supply Chain Marketing & Sales Customer Experience
• Product Design & Engineering
• Component Development
• System Integration
• Customer Engagements
• Pricing Strategies
• Sales forecast
• System Integration
• Interaction• Inventory Management & Quality
Control
Companies across various sectors are assessing the impact of AI in the
automotive industry and are accelerating acquisition strategies in the domain
to unlock new AI-based business opportunities. The increased momentum of the
M&A activity is a key evidence of how organizations are focusing on AI strategies
to reap major benefits as the new trends in the automotive sector proliferate.
Strategic M&A for AI technologies in the automotive sector is extending across the
value chain with key technology areas impacting different functional areas. The
acquisition trends are bringing business transformations with the incorporation of
new business models around AI products and solutions.
Detailed below is a comprehensive perspective of the different technologies
that are being acquired in automotive manufacturing, operations, supply chain,
marketing, sales and for customer-experience and support. This signifies that
automotive companies and entities from adjacent markets are aiming to lay the
foundation of AI-enabled solutions in the entire ecosystem.
COMPUTER VISION
AI HARDWARE & SOFTWARE
SELF-DRIVING SOFTWARE STACK
MAPPING TECHNOLOGY
GESTURE RECOGNITION
AI-BASED ANALYTICS
CLOUD SERVICES
DATA TRAINING
AI-BASED ANALYTICS
HIGH ACTIVITY MEDIUM ACTIVITY LOW ACTIVITY
KEY AI-BASED TECHNOLOGY AREAS IN THE AUTOMOTIVE VALUE CHAIN

Companies are also following different integration strategies for these AI acquisitions for their business unit and functions. Integration strategies deployed by
the acquirers are largely dependent on the degree of integration required and the nature of the deal. The effectiveness of the transactions is evident through the
operational synergies due to the integration strategies. According to the M&A trend in AI in automotive space, the transactions can be outlined into five major
categories as shown in Table 1.
The subsequent sections of the report provide a detailed trend analysis of these acquisitions with a focus towards key technology areas.
Tuck-in Acquisitions
TYPE OF DEAL
Business Line Expansion
Vertical Integration
Horizontal Integration
Defensive Strategy
Capability Addition
INTENT
Largely Complementary
Stack Expansion
Reduced Competition
Retain Market Share
Large company acquiring smaller companies to acquire
capabilities like technology, patents, skills, customers, etc.,
focusing on the integration of gained capabilities into the
acquirer’s operating model.
DESCRIPTION
Buying company to get into a new line of business and
build new business model
Acquiring a company which is a supplier to the buyer or a
downstream service provider within the value chain
Acquiring a company that is operating at the same level in
the value chain or in the area of an industry
Acquiring a company to maintain position in the value chain
Majority of the deals are tuck-in type;
Apple acquired Drive.ai and VocalIQ; Intel
acquired Mobileye; Velodyne acquired
Mapper.ai
EXAMPLE
Samsung acquired Harman International
Xilinx buying Deephi
Nuance acquired Voicebox Technologies
Ola acquired Pikup.ai
Table 1 : Integration Strategies in AI in Automotive

The study reports how M&A activities are shaping up around AI in the automotive industry and covers the entire value chain. A
comprehensive approach was followed to understand the trend, and included :
1. Secondary research from sources freely available online. The keywords used included different synonyms of the trending
artificial intelligence algorithms and captured aspects related to the different segments of the automotive industry.
Company-based search was also conducted to check the acquisitions by major automakers, auto suppliers, and other key
participants in the automotive value chain.
2. In-depth searches around the key AI technology trends to ensure that all relevant technical parameters were considered in
the analysis.
3. Trend analysis of key technology areas impacting the value chain.
4. Snapshots of post-acquisition scenarios, wherever possible, for the AI transactions using references from the company’s
official announcements and annual reports.
5. Detailed gap analysis to pinpoint the AI technology areas where acquisitions have not yet taken place, and could be potential
areas of interest for automotive companies and investors in the future.
Note: Acquisitions related to LiDAR components and autonomous guided vehicle were not considered within the scope of the study.
METHODOLOGY
FOR M&A ANALYSIS

M&A ANALYSIS OF AI IN AUTOMOTIVE
TECHNOLOGY TRENDS

No.OfDeals
+82%
+6.67%
Years
A total of 67 deals were considered for the analysis. The graph show the number of AI
companies acquired over the years. It indicates that interest in AI technologies for automotive
industry begun only after 2015 when the trend of self-driving vehicles started to do rounds. The
count of deals was highest in the year 2017 and has been consistent thereafter.
*Deal amounts are in USD
AI IN AUTOMOTIVE:
M&A TREND OVER THE YEARS
PROMINENT HIGH VALUE DEALS IN 2017
ACQUIRERS
ACQUIRERS
ACQUIRERS
#
#
#
TARGET
TARGET
TARGET
DEAL AMOUNT
DEAL AMOUNT
DEAL AMOUNT
Intel
Blackberry
Faurecia
1
1
1
2
2
2
3
3
3
4
4
4
5
5
Mobileye
Cylance
Clarion
15.3 Bn
1.4 Bn
1.3 Bn
Samsung
Xilinx
Lear Corporation
Harman International
DeePhi
Xevo
8 Bn
300 Mn
320 Mn
450 Mn
300 Mn
300 Mn
Aptiv
Ansys
Appen
NuTonomy
Optis
Figure Eight
Continental
Cars.com
AB Dynamics plc
Argus Cyber Security
Dealer Inspire
rFpro
430 Mn
165 Mn
27 Mn
IHS Markit
Nuance
Communications
automotiveMastermind
Voicebox Technologies
392 Mn
82 Mn

ACQUIRER LANDSCAPE
The ‘Others’ category in the graph include machine vision system provider, digital marketing
companies, conversational AI software provider, insurance company, supply chain
management company, LiDAR manufacturers, wireless network engineering firm, telematics
provider, IT service provider, fleet management companies and navigation and mapping
software providers.
Companies across the automotive value chain have made
acquisitions focused on AI technologies. This pie chart highlights the
fact that automakers are buying fewer AI entities as compared to
automotive suppliers.
Automotive suppliers are leading the acquisition curve, with a focus on
acquiring capabilities related to computer vision, gesture recognition,
AI-based analytics, and self-driving software stacks. Automakers,
however, are actively approaching investments and collaboration
routes for intelligent product development.
Semiconductor firms hold the second position amongst the acquirers
in the AI automotive space. These companies are adopting innovative
investment strategies in their bid to acquire a strong market share
in the mobility domain. Several well-known technology giants also
feature in the top acquirer category, with acquisitions focused on
conversational AI and computer vision, while leading communication
service providers are targeting companies leveraging AI capabilities
for cloud, security and antenna systems. Additionally, key electronics
companies are acquiring entities for inorganic growth to cater to
applications around telematics and smart cockpit solutions.
Acquirer Distribution In The Acquisition Trend In AI
Automotive Space

Multiple deals have been led by companies from the categories
of car dealerships, car retailers, and cab aggregators. Acquirers
from adjacent markets include categories such as food delivery
firms, data intelligence companies, and insurance providers,
amongst many others.
Ford
Intel
Baidu
Samsung
Panasonic
Daimler
Continental
Valeo
Uber
Apple
Doordash
3
3
3
2
2
2
2
2
2
2
2
Companies With Multiple Acquisitions
AUTOMOTIVE
SUPPLIERS
TEST SYSTEM
SUPPLIES
OTHERS
16% of the deals highlight multiple
acquisitions done by acquirers across
the value chain.
CAR RETAILERS
SEMICONDUCTOR
COMPANIES
COMMUNICATION
SERVICE PROVIDER
ELECTRONICS
COMPANY
AUTOMOTIVE
MANUFACTURERS
DATA INTELLIGENCE
COMPANIES
CAR AGGREGATORS
TECHNOLOGY
GIANTS
AUTOMOTIVE
MARKETPLACE
FOOD DELIVERY
SYSTEM

TECHNOLOGY BREAKDOWN
A majority of the acquisitions in the automotive sector have been undertaken for
XXXXXXX capabilities. The distribution of deals for computer vision has been uniform
for almost last five years. Transactions related to AI-based analytics started in
2017 and have been a key area of interest for entities from different sections of the
value chain. The growing demand for intelligent cockpit and connected passenger
experience is evident through multiple deals done for conversational AI capabilities.
XXXXXXX, and XXXXXXX, are the two other areas that feature among the top
categories. Other technology areas of interest include XXXXXXX, XXXXXXX, XXXXXXX,
XXXXXXX, XXXXXXX and XXXXXXX. In the first half of 2019, the major acquisitions have
been around XXXXXXX , XXXXXXX , XXXXXXX , and XXXXXXX .
* The ‘xxxxxxx’ category in the graph includes acquisitions related to virtualization, car lock, human-machine interface, software defined solutions and predictive road-sensing mechanism.
XXXXXXX
14 13 6 5 4
3
3
4
2
2
6 5
XXXXXXX XXXXXXX
XXXXXXX XXXXXXX
XXXXXXX
XXXXXXX XXXXXXX
XXXXXXX
XXXXXXX XXXXXXX XXXXXXX

Detailed Technology Breakdown Over the Years
2010 2015 2016 2017 2018 2019
Years
TechnologyCoverageintheTrend
10%
20%
0%
30%
40%
50%
60%
70%
80%
90%
100%

ACQUISITION TREND #1:
COMPUTER VISION
Car manufacturers, auto suppliers, semiconductor companies, car retailers, cab
aggregators, food delivery firm, and technology leaders have made acquisitions for
computer vision capabilities.
Acquisition Trend in Computer Vision
Source: Netscribes’ Secondary Research
20162015 2017 2018 2019
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Computer vision constitutes a major trend in the AI automotive M&A trend analysis.
The technology, a core solution for addressing the obstacles in the practical
implementation of autonomous vehicles has garnered interest from acquirers
across the automotive value chain, and has been a major research topic for the last
few years.
Computer vision involves analysis of image data or visual sequences to classify
objects within a field of vision of a vehicle. The analysis includes application of
algorithms for image segmentation, scene parsing, 3D modeling, pattern recognition,
3D reconstructions, motion analysis, and object recognition. Machine learning
models and neural networks are being used for enabling computer vision solutions.
The automotive sector is facing several challenges with the implementation of
computer vision. -------------------------------------------------------------
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Such technological gaps in the portfolio of the different industry players in the
automotive sector is driving the need to acquire companies that complement the
acquirer’s solutions.
M&A trend in the computer vision domain highlights acquisition of target companies
addressing some of these requirements. ----------------------------------------
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The transactions in computer vision have been detailed in Table 2. The category of
acquirers in this domain include:
• Automotive suppliers – Xevo, Magneti Marelli, Panasonic
• Semiconductor companies – Intel, Xilinx
• Vision system providers – Cognex, Ambarella
• Car retailer – Carvana
• Automaker – Ford
• Cab Aggregator – Ola
• Food Delivery Firm – DoorDash
• Technology Giant – Baidu

• Object Detection and Classification
• Image Analysis
• Augmented Reality
• Depth Perception
• Compression and Pruning
• Three-dimensional Modeling
COMPUTER VISION

Ola Cabs
Apple
DoorDash
ACQUIRER
Pikup.ai
Drive.ai
lvl5
August 2019
June 2019
April 2019
TARGET COMPANY DATE
Ola, an Indian cab aggregator, acquired Pikup.ai to accelerate the adoption of mobility solutions in
the region. The target company has expertise in computer vision and sensor fusion technologies.(1)
Apple acquired Drive.ai, a startup that develops AI software for autonomous vehicles by leveraging
deep learning mechanisms. The company offers on-demand self-driving services, and has piloted
its solution in Arlington, Texas. (2)
Drive.ai focuses on developing software kits for converting normal cars into autonomous systems.
The company’s patent portfolio concentrates on autonomous navigation, road sensing/mapping
techniques using AI, autonomous processing of rideshare requests, training AI for object detection,
and methods for communicating state, intent, and context of an autonomous vehicle. This
acquisition provides Apple with the required skillsets for its autonomous driving project.
lvl5 has developed AI-based computer vision techniques for creating HD maps from crowdsourced
image data, which can be obtained from regular or smartphone cameras. DoorDash acquihired
lvl5’s cofounders and core team through this acquisition.(3)
OVERVIEW OF THE DEAL
Table 2: Acquisition Trend in Computer Vision
Magneti Marelli Smart Me Up August 2018 Magneti Marelli acquired Smart Me Up, a startup providing perception software for autonomous
vehicles. The target company’s computer vision technology boasts low computing power and low
heat generation, and analyses data from different sensors like camera, LiDAR or RADAR for providing

ACQUIRER TARGET COMPANY DATE OVERVIEW OF THE DEAL
perception data in real time. AI technology is embedded on a chip, and can also be used for driver
monitoring application.(4)
With this acquisition, the buyer has acquired a talent pool of experienced scientists and engineers in
embedded perception techniques.
Xilinx DeePhi
Technology
July 2018 Xilinx acquired DeePhi, an AI-based software solutions provider for its specialization in machine
learning, deep compression, pruning, and system-level optimization for neural networks. The
acquired capabilities are enabling Xilinx to cater to the demand from IoT devices, telecom, consumer
goods, aerospace, and autonomous vehicle. Deephi’s AI software stack, based on sparse neural
network, is used for computer vision applications in autonomous vehicle and video surveillance
applications. It includes deep compression algorithm (using reinforcement models), which provide
higher accuracy and increased frames per second, while reducing power consumption. The
technology provides improved SSD (Single Shot MultiBox Detector) using VGGNet (Vanishing Point
Guided Network for Lane and Road Marking Detection and Recognition) for 2D object detection.(5)
Prior to the acquisition, Deephi was in a close collaboration with Xilinx on semiconductor level system
optimization, and was using Xilinx’s FPGA platform. The acquired technologies will allow Xilinx to enter
the autonomous driving race.
Carvana CAR360 April 2018 Carvana acquired CAR360 to address issues related to inventory management and vehicle
photography essential for enabling an effective and trustworthy e-commerce platform for buying
and selling cars. Car360’s AI engine integrates with Carvana’s 3D vision and AR techniques in an
app for streamlining inventory management, delivering an interactive experience and 360-degree
car viewing solution.(6)

Acquiring new capabilities are advancing technology development for some buyers while some are yet to achieve new business opportunities
COMPUTER VISION:
POST-ACQUISITION SCENARIO
New Product Development AI Integration for Automotive Slow Research & Development Competitive Edge
Co-developing Interior rear-view
mirror with electronic toll collection
for BMW X5 in Japan
Developed Cognex-VIDI deep-
learning software suite for
defect detection, classification
& assemble verification for
automotive manufacturing
Co-developing interior rear-view
mirror with electronic toll collection
for BMW X5 in Japan
Integrated Deephi’s deep
compressions models on Xilinx’s
FPGA addressing AI hardware
challenges and creating new
monetization options

• Automakers are majorly adopting partnership and investment strategies for computer vision capabilities.
• Auto suppliers are acquiring computer vision companies to shift business models towards deployment of autonomous vehicle.
• Semiconductor entities are acquiring computer vision software capabilities to address hardware incompatibility issues and
develop full-stack solutions.
• Ride-hailing company Ola Cabs has acquired computer vision capabilities to stay relevant in the Indian mobility market.
• Besides autonomous vehicles, computer vision is being pursued for inventory management, inspection and fault detection
during manufacturing and in food delivery business.
KEY TAKEAWAYS

INSIGHTS & RECOMMENDATIONS
INSIGHTS & RECOMMENDATIONS

ACQUISITION GAP ANALYSIS
AND FUTURE GROWTH OPPORTUNITIES We found that there are some technology areas that are yet to be in focus for
AI integration in automotive applications and can be considered as potential
target areas. Potential target technologies and companies with significant
value proposition for automotive industry have been highlighted in the
next section. For companies in search of business opportunities, there are
a number of potential technology areas they can consider for investment,
collaborations or acquisition in the future.

Potential Target Technology Areas
Neuromorphic
Technology
XXXXXXX
Investment in neuromorphic solutions for deriving inference in real time for self-driving cars is a strategic route for long-term
benefits. While the technology is still at an early stage, researchers and companies developing these solutions are aiming
to build neuromorphic device for complex traffic scenarios. Spiking neural network that allows event-driven processing
is driving neuromorphic architectures. Companies providing neuromorphic vision systems can also be considered as
potential target entities in the future.
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INVESTMENT STRATEGY:
TARGET COMPANIES:
TARGET COMPANIES:
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TARGET COMPANIES:
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TARGET COMPANIES:
XXXXXXX
Companies are developing deep reinforcement learning models in combination with -------------------------------------
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XXXXXXX
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TARGET COMPANIES:
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TARGET COMPANIES:
XXXXXXX
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XXXXXXX
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TARGET COMPANIES:
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TARGET COMPANIES:
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XXXXXXX

TARGET COMPANIES:
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CONCLUDING REMARKS
The M&A analysis in the AI automotive space highlights the different strategies
adopted by the key industry participants. These companies are driving acquisitions
in machine learning technologies after evaluating their current offerings and
scanning the requirement of AI integration for capturing market share in the future.
Automotive suppliers, OEMs, and automakers are targeting startups and innovators
that are driving disruptions -------------------------------------------------
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----------------------------------------------------------------------------
----------------------------------------------------------------------------
--------------------------------------------- Such alternates can help create
monetization opportunities for both OEMs and the different merchants in the AI
automotive market. Opportunity also exists for companies developing AI-based
---------------------------------------------------------------------------
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---------------- for developing their customer-facing activities.
XXXXXXX has been the leading technology trend in the overall analysis for
manufacturing of secure self-driving cars. In terms of creating the infrastructure
for these cars, acquisitions related to AI solutions for ------------------------
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------------ in the future. Interest from semiconductors will also expand from
computer vision, AI hardware, and analytics to --------------------------------
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compression technique requirements.
Technology giants such as Baidu and Apple are approaching a safe strategy by
acquiring key and low risk capabilities in --------------------------------------
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Obtaining raw data from various sensors including LiDAR, RADAR, and camera,
-------------------------------------------------------------------------
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------------------------------------.
Few transactions have been done by insurance providers, LiDAR companies,
supply chain companies, IT service providers and MNOs. These entities are majorly
adopting a collaborative path for catering to the AI automotive market.
The M&A participants are focused on ADAS segments, while actively -------------
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-----------
Overall, acquisitions related to AI technologies are expected to play a primary role
in the automotive sector to gain complementary skillsets. -----------------------
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------------------------------------- other mobility applications.

REFERENCES
1. https://www.analyticsindiamag.com/ola-acqui-hires-bengaluru-based-ai-startup-pickup-ai/
2. https://www.drive.ai/
3. https://www.lvl5.ai/
4. https://www.magnetimarelli.com/press_room/news/magneti-marelli-signs-agreement-aimed-acquisition-smartmeup-french-company-focused
5. https://www.xilinx.com/publications/events/developer-forum/2018-frankfurt/xilinx-machine-learning-strategies-with-deephi-tech.pdf
6. https://www.car360.com/
7. https://www.cognex.com/company/press-releases/2017/cognex-acquires-maker-of-deep-learning-software-for-industrial-machine-vision
8. https://www.cognex.com/products/machine-vision/vision-software/visionpro-vidi
9. https://www.cognex.com/products/leading-technology/deep-learning-based-image-analysis
10. https://www.reuters.com/article/us-baidu-m-a-idUSKBN17F0JF
11. https://www.ficosa.com/news/ficosa-presents-latest-technology-connected-autonomous-car-mwc-2017/
12. http://thetechnews.com/2016/12/18/xevo-to-acquire-surround-io-to-educate-the-connected-car-with-artificial-intelligence/
13. https://www.saips.co.il/
14. https://www.ficosa.com/products/adas/machine-vision/
15. https://newsroom.intel.com/editorials/intel-acquires-computer-vision-for-iot-automotive/#gs.0uoob5
16. https://www.ambarella.com/news/ambarella-acquires-vislab-a-european-developer-of-computer-vision-and-intelligent-automotive-control-systems/
17. http://www.drust.com/en/industries/insurance.html

REFERENCES
18. XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

REFERENCES

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