Automotive Artificial Intelligence (AI) Market by Offering, Technology (Deep Learning, Machine Learning, Computer Vision, Context-aware Computing and Natural Language Processing), Process, Application, Component and Region - Global Forecast to 2027
Updated on : Oct 25, 2024
Factors such as rising demand for enhanced user experience and convenience features and the growing adoption of ADAS technology by OEMs are driving the growth of the automotive artificial intelligence industry during the forecast period.
Automotive Artificial Intelligence (AI) Market Size & Growth
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Automotive Artificial Intelligence (AI) Market Dynamics
Driver: Rising demand for enhanced user experience and convenience features
Human-machine interface (HMI) solutions for the automotive industry have become easier to control and operate, thus enhancing user experience. Such solutions can give a user control over applications such as music systems, vehicle lights, and infotainment systems.
Earlier, the electronics system in a vehicle accounted for just 1–2% of the vehicle cost, but due to the increasing demand for enhanced user experience and convenience features, the share has increased to 8–12%.
Modern vehicles frequently incorporate driver assistance technology, which heavily relies on AI. It accurately detects whether a driver is sleepy or fatigued by watching their eyes. To make commuting more pleasurable, simple, and less tiring, the car's AI system learns everything about the driver, including preferred temperature settings, songs, and destinations. Automotive companies work with software providers to ensure that AI makes the interior atmosphere ideal for the driver, leading to engaging and individualized user experiences.
Restraint: Increase in the overall cost of vehicle
Autonomous vehicles are anticipated to be highly-priced chiefly due to the introduction of new commercialized technological systems. Most of the advanced technologies are embedded in luxury and premium cars, which have a limited customer base owing to their high price.
Hence, a high vehicle cost is likely to dampen market growth. The demand for expensive autonomous vehicles is anticipated to be moderate, as compared to semi-autonomous vehicles. For autonomous cars to be effective, the infrastructure must also support the technology. For instance, lane assist technology requires lane lines on the road for the system to sense and adjust vehicle position. This in turn is expected to increase the infrastructure development cost.
The installation of advanced features such as blind spot detection (BSD), lane departure warning (LDW), adaptive cruise control (ACC), and forward collision warning system (FCWS) eventually increases the overall cost of the vehicle. While cost is not an area of concern for premium cars, the demand for small and medium segment cars is affected by the cost of the vehicle. Every automaker is trying to provide efficient safety features at a reasonable price. The increasing cost of vehicles owing to enhanced safety features may restrain the growth of the automotive AI market.
Opportunity: High potential of in-car payments
Among the newest developments, in-car payments are designed to revolutionize how customers refuel, pay for parking or tolls, and possibly even how users do their grocery shopping.
Open Banking, which enables clients to pay directly from their bank and reduces any friction or security risks in their payment journey, could be used to implement in-car wallets and payment systems. By streamlining transaction fees and providing a more seamless consumer experience overall, would reduce the need for third-party payment networks. For in-vehicle payment systems, fuel, tolls, and parking are the three most frequent usage applications.
The adoption of connected vehicles is accelerating in key European markets like Germany, France, and the UK. The rapid expansion of in-car payments will also be fueled by developments in AI for natural language processing and the deployment of voice assistants in automobiles. Strategic collaborations can help businesses integrate in-car payments. The two biggest payment processors in North America, Visa, and Mastercard, are working intensively with vehicle manufacturers to develop and implement in-car payment systems. In 2019, Hyundai partnered with Xevo, a connected-car technology leader to develop a telematics platform that includes a digital payment feature.
Challenge: Effect of unfavorable weather on sensors
One of the main difficulties with driverless automobiles is bad vision. Self-driving cars use a variety of sensors, including camera sensors, radars, and lidars, to detect pedestrians, cyclists, or other vehicles on the road, and gauge their speed and distance.
A self-driving car's control system receives data from the sensors, and t processes it. The system then determines whether to stop, turn left or right, proceed, or, if necessary, shift into reverse. However, the sensors have a hard time operating well in the presence of snow, fog, or a lot of rain. The safety of the driver may be at risk since the accuracy of the sensing capability is significantly influenced by unfavorable weather.
Technology developments are likely to help overcome these challenges, allowing completely autonomous vehicles to operate in all kinds of weather. Autonomous driving is anticipated to transform human existence by enhancing road efficiency, decreasing accidents, increasing productivity, and doing so with reduced negative environmental impact.
GPU component to hold the largest share of automotive artificial intelligence market
During the forecast period, GPU held the largest share of the AI in automotive market. In the rapidly developing fields of autonomous driving and advanced driver assistance systems (ADAS), GPUs are becoming more and more important.
To process and analyze sensor data in real-time, ADAS platforms can make use of the GPU's graphics computation capacity. The majority of modern cars now have GPUs installed to support digital dashboards with several high-resolution screens that display maps, forecasts, and other visual information while driving. In mid-range vehicles, 1080p resolution is becoming common, and 4K TVs are becoming more frequent in luxury and executive vehicles.
AI in Automotive Market Share
Human-machine interface (HMI) application to hold the largest share of the AI in automotive market
In a vehicle, HMI allows the driver and the passenger to interact with the vehicle by seamlessly delivering convenience, information, and entertainment. Major components of HMI include electromechanical devices such as keypads, pointing devices, indicators, and alarms.
The infotainment category comprises of features such as speech recognition, eye tracking, monitoring driving, gesture recognition, and a database of natural languages. Advanced HMI solutions are being incorporated by OEMs to deliver a new and unique experience to users and help differentiate their brand image. The intelligent car concept is moving rapidly from the drawing boards to the streets.
It will give users effective vehicle controls such as self-braking, advanced cruise control, and self-parking. These developments will create plenty of opportunities for automotive HMI systems. Advanced HMI systems are also in demand in developed countries where people prefer to travel long distances in their own vehicles. Improved infrastructure can also be a prime reason for this growing trend.
Machine learning holds the second largest share of the artificial intelligence in automotive market
Machine learning gives cars the ability to analyze and learn from different driving situations, thus helping to reduce accidents and make cars safer and more efficient. It can create accurate models that can guide future actions and rapidly identify patterns at a scale not previously achievable.
Machine learning has various technologies such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. Machine learning methods are particularly applicable when it comes to powering the system with new insights within the auto industry because the data sets are large, diverse, and change quickly.
Automotive Artificial Intelligence (AI) Market Regional Analysis
North America is expected to be the major contributor to the automotive AI market
During the forecast period, North America held the largest share of the artificial intelligence in automotive market in 2027 and is expected to continue its upward growth trend.
Automotive artificial intelligence is expected to have moderate growth in this region driven by the rapid development of autonomous vehicle technology, and the strict government regulations related to road safety. The region is home to a large number of leading technological firms, which enables the early introduction and high adoption of technologies such as automotive artificial intelligence.
Government incentives and funding play a major role in the development of this technology. The automotive industry in the US is highly advanced, with the ‘Big Three’-Ford Motor Company, General Motors, and Fiat-Chrysler Automotive-continuously upgrading their product portfolios. Vehicles in the US are equipped with advanced features such as adaptive cruise control, lane departure, warning systems, voice recognition system, gesture recognition, and blind spot detection.
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Top Automotive Artificial Intelligence (AI) Companies - Key Players
The automotive artificial intelligence companies are
- Nvidia Corporation (US),
- Alphabet Inc. (US),
- Intel Corporation (US),
- Microsoft Corporation (US),
- IBM Corporation (US),
- Qualcomm Inc. (US),
- Tesla Inc. (US),
- BMW AG (Germany),
- Micron Technology (US), and
- Xilinx Inc. (US).
Automotive Artificial Intelligence (AI) Market Report Scope
|
Report Metric |
Details |
|
Estimated Market Size |
USD 2.3 Billion in 2022 |
|
Projected Market Size |
USD 7.0 Billion by 2027 |
|
Automotive Artificial Intelligence Growth Rate |
24.1% |
|
Automotive AI Market Size Available for Years |
2018–2027 |
|
Base Year Considered |
2021 |
|
Forecast Period |
2022–2027 |
|
Forecast Units |
Value (USD Million/Billion) and Volume (Thousand Units) |
|
Segments Covered |
|
|
Geographies Covered |
|
|
Companies Covered |
|
| Key Market Driver | Rising demand for enhanced user experience and convenience features |
| Key Market Opportunity | High potential of in-car payments |
| Largest Growing Region | North America |
| Largest Market Share Segment | Machine Learning |
| Largest Application Market Share | Human-machine interface (HMI) application |
The study categorizes the automotive artificial intelligence market based on Offerings, Technology, Process, Application, Components, And Region.
By Offering:
- Hardware
- Software
By Technology:
- Deep Learning
- Machine Learning
- Context- aware Computing
- Computer Vision
- Natural Language Processing
By Process:
- Signal Recognition
- Image Recognition
- Data Mining
By Application:
- Human–Machine Interface
- Semi-autonomous Driving
- Autonomous Driving
- Identity Authentication
- Driver Monitoring
- Autonomous Driving Processor Chips
By Component:
- Graphics processing unit (GPU)
- Microprocessors (Incl. ASIC)
- Field Programmable Gate Array (FPGA)
- Memory and Storage systems
- Image Sensors
- Biometric Scanners
- Others
By Region:
- North America
- Europe
- Asia Pacific
- RoW
Recent Developments in Automotive Artificial Intelligence (AI) Industry
- In June 2022, Qualcomm launched 4th gen Snapdragon Automotive Cockpit Platforms. The digital cockpit platform makes use of 5nm processing technology and aims to give automakers access to one of the highest performance system-on-chips (SoCs).
- In February 2021, Micron technology launched LPDDR5 which is compatible with ADAS (advanced driver assistance system) features like lane departure warning, automatic emergency braking, adaptive cruise control, and blind spot detection.
- In June 2021, Micron technology launched UFS 3.1 storage. It offers crucial high-throughput and low-latency storage as infotainment systems develop to include high-resolution screens and human-machine interface capabilities based on artificial intelligence (AI).
- In December 2019, Google launched AutoML Natural Language, a natural language processing extension of its Cloud AutoML machine learning platform. The product provides global support to its customers to perform tasks such as classifying, detecting, and analyzing sentiments, entities, content, and syntax.
Frequently Asked Questions (FAQ):
How big automotive artificial intelligence market?
The automotive artificial intelligence market size is valued at USD 2.3 Billion in 2022 and is anticipated to be USD 7.0 Billion by 2027; growing at a CAGR of 24.1% from 2022 to 2027.
Which is the potential market for automotive artificial intelligence solution in terms of region?
The North America region is expected to dominate the automotive artificial intelligence market due to the technological developments of autonomous vehicles and vehicles in the US are equipped with advanced features such as adaptive cruise control, lane departure, warning systems, voice recognition system, and gesture recognition, and blind spot detection.
Which is the key technology that dominates automotive artificial intelligence market?
The key companies have been focusing on collaborations and partnerships to significantly grow in the automotive artificial intelligence market.
What are the key strategies adopted by key companies in the automotive artificial intelligence market?
The key companies have been focusing on collaborations and partnerships to significantly grow in the automotive artificial intelligence market.
Which are the major companies in the automotive artificial intelligence market?
Nvidia Corporation (US), Alphabet Inc. (US), Intel Corporation (US), Microsoft Corporation (US), IBM Corporation (US), Qualcomm Inc. (US), Tesla Inc. (US), BMW AG (Germany), Micron Technology (US), and Xilinx Inc. (US) are the players dominating the global automotive artificial intelligence market.
What small company will be manufacturing the semiconductors for AI cars?
Autotalks, ArgoAI, Geo Semiconductor, Indie semiconductor, NIO, and Nikola are among the small companies that will be manufacturing semiconductors for AI cars.
How much is AI involved with the setup of the f1 autos?
Artificial intelligence in F1 autos allows for correctly analyzing data for improving the car's performance. Around 200 sensors in an F1 car measure many aspects of the vehicle. Onboard sensors provide this data, which can include temperature, pressure, frequency, and speed. verything can be enhanced, including engine performance, tire grip, brake operation, speed, telemetry, and fuel efficiency and all of this data is streamed into massive servers for analysis. The data can be utilized for improving the performance of F1 autos such as steering, acceleration, braking, lap times, top speeds, and pit stop durations. This could result in enhanced components, improved aerodynamic performance, or even new engine
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TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 32)
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 REGIONAL SCOPE
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 36)
2.1 RESEARCH DATA
FIGURE 1 PROCESS FLOW: AUTOMOTIVE ARTIFICIAL MARKET SIZE ESTIMATION
FIGURE 2 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET: RESEARCH DESIGN
2.1.1 SECONDARY AND PRIMARY RESEARCH
2.1.2 SECONDARY DATA
2.1.2.1 List of major secondary sources
2.1.2.2 Secondary sources
2.1.3 PRIMARY DATA
2.1.3.1 Primary interviews with experts
2.1.3.2 Key industry insights
2.1.3.3 Breakdown of primaries
2.1.3.4 Primary sources
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Estimating market size using bottom-up approach (demand side)
FIGURE 3 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to capture market share using top-down analysis (supply side)
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
FIGURE 5 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
TABLE 1 ASSUMPTIONS FOR RESEARCH STUDY
2.5 RISK ASSESSMENT
2.5.1 LIMITATIONS AND ASSOCIATED RISKS
TABLE 2 LIMITATIONS AND ASSOCIATED RISKS
2.6 RESEARCH LIMITATIONS
FIGURE 6 LIMITATIONS FOR RESEARCH STUDY
TABLE 3 MARKET FORECASTING METHODOLOGY ADOPTED FROM 2022 TO 2027
3 EXECUTIVE SUMMARY (Page No. - 49)
FIGURE 7 SOFTWARE SEGMENT TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET DURING FORECAST PERIOD
FIGURE 8 AUTOMOTIVE AI MARKET, BY TECHNOLOGY, 2022–2027
FIGURE 9 AUTOMOTIVE AI MARKET, BY PROCESS, 2022 VS 2027
FIGURE 10 AUTOMOTIVE AI MARKET, BY APPLICATION, 2022–2027
FIGURE 11 AUTOMOTIVE AI MARKET, BY REGION, 2022
4 PREMIUM INSIGHTS (Page No. - 54)
4.1 ATTRACTIVE OPPORTUNITIES FOR AUTOMOTIVE AI MARKET PLAYERS
FIGURE 12 RISING INDUSTRIAL AUTOMATION TO DRIVE MARKET GROWTH
4.2 AUTOMOTIVE AI MARKET, BY OFFERING
FIGURE 13 SOFTWARE SEGMENT TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET DURING FORECAST PERIOD
4.3 AUTOMOTIVE AI MARKET, BY TECHNOLOGY
FIGURE 14 DEEP LEARNING SEGMENT TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET FROM 2022 TO 2027
4.4 NORTH AMERICAN AUTOMOTIVE AI MARKET, BY APPLICATION AND COUNTRY
FIGURE 15 HMI SEGMENT AND US HELD LARGEST SHARES OF NORTH AMERICAN AUTOMOTIVE AI MARKET IN 2027
4.5 AUTOMOTIVE AI MARKET, BY COUNTRY
FIGURE 16 CANADA TO REGISTER HIGHEST CAGR IN AUTOMOTIVE AI MARKET BETWEEN 2022 AND 2027 (IN TERMS OF VALUE)
5 MARKET OVERVIEW (Page No. - 57)
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
FIGURE 17 AUTOMOTIVE AI MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND RESTRAINTS
5.2.1 DRIVERS
5.2.1.1 Growing adoption of ADAS technology by OEMs
FIGURE 18 ROAD TRAFFIC DEATH RATE, PER 100,000 POPULATION, 2019
TABLE 4 REGULATIONS FOR DRIVER ASSISTANCE SYSTEMS
5.2.1.2 Rising demand for enhanced user experience and convenience features
5.2.1.3 Emerging trend of autonomous vehicles
FIGURE 19 LEVELS OF AUTONOMOUS DRIVING
TABLE 5 AUTONOMOUS DRIVING INITIATIVES BY AUTOMAKERS
5.2.1.4 Increasing use of AI to make buying decisions
FIGURE 20 DRIVERS FOR AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET AND THEIR IMPACT
5.2.2 RESTRAINTS
5.2.2.1 Increase in overall cost of vehicles
5.2.2.2 Threat to vehicle-related cybersecurity
5.2.2.3 Inability to identify human signals
FIGURE 21 RESTRAINTS FOR MARKET AND THEIR IMPACT
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing demand for premium vehicles
5.2.3.2 Growing need for sensor fusion
FIGURE 22 SENSOR FUSION
5.2.3.3 High potential of in-car payments
FIGURE 23 OPPORTUNITIES FOR MARKET AND THEIR IMPACT
5.2.4 CHALLENGES
5.2.4.1 Difficulty in maintaining cost-quality balance
5.2.4.2 Effect of unfavorable weather on sensors
FIGURE 24 CHALLENGES FOR MARKET AND THEIR IMPACT
5.3 PORTER’S FIVE FORCES ANALYSIS
5.3.1 BARGAINING POWER OF SUPPLIERS
5.3.2 BARGAINING POWER OF BUYERS
5.3.3 THREAT OF NEW ENTRANTS
5.3.4 THREAT OF SUBSTITUTES
5.3.5 INTENSITY OF COMPETITIVE RIVALRY
TABLE 6 IMPACT OF EACH FORCE ON AUTOMOTIVE AI MARKET
5.4 PRICING ANALYSIS
FIGURE 25 ASP OF PROCESSORS, 2018–2021 (USD)
TABLE 7 ASP RANGE OF PROCESSOR TYPES, 2018–2021 (USD)
5.5 TRADE ANALYSIS
5.5.1 EXPORT SCENARIO FOR AUTOMATIC DATA PROCESSING MACHINES
TABLE 8 AUTOMATIC DATA PROCESSING MACHINES EXPORT, BY KEY COUNTRY, 2017–2021 (USD THOUSAND)
5.5.2 IMPORT SCENARIO FOR AUTOMATIC DATA PROCESSING MACHINES
TABLE 9 IMPORT DATA: AUTOMATIC DATA PROCESSING MACHINES, BY KEY COUNTRY, 2017–2021 (USD THOUSAND)
5.6 ECOSYSTEM
FIGURE 26 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET: ECOSYSTEM ANALYSIS
TABLE 10 AUTOMOTIVE AI MARKET ECOSYSTEM
5.7 CASE STUDY ANALYSIS
5.7.1 AFFECTIVA DEVELOPS SYSTEM TO DETECT DRIVER BEHAVIOR
5.7.2 VOLVO USES MACHINE LEARNING-DRIVEN DATA ANALYTICS TO PREDICT BREAKDOWNS AND FAILURES
5.7.3 ROLLS-ROYCE USES MICROSOFT CORTANA INTELLIGENCE FOR PREDICTIVE MAINTENANCE
5.7.4 AUDI USES AI SYSTEM TO ALERT DRIVER
5.8 PATENT ANALYSIS
FIGURE 27 NUMBER OF PATENTS GRANTED PER YEAR OVER LAST 10 YEARS
TABLE 11 TOP 10 PATENT OWNERS
FIGURE 28 COMPANIES WITH HIGHEST NUMBER OF PATENT APPLICATIONS IN LAST 10 YEARS
TABLE 12 IMPORTANT PATENT REGISTRATIONS, 2020–2022
FIGURE 29 REVENUE SHIFT IN AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET
5.9 REGULATORY LANDSCAPE
5.9.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 13 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 14 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 15 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 16 REST OF THE WORLD: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.10 REGULATORY STANDARDS
5.10.1 GENERAL DATA PROTECTION REGULATION (GDPR)
5.10.2 STANDARDS IN ITS/C-ITS
TABLE 17 SECURITY AND PRIVACY STANDARDS DEVELOPED BY EUROPEAN TELECOMMUNICATION STANDARDS INSTITUTE (ETSI)
5.11 VALUE CHAIN ANALYSIS
FIGURE 30 AUTOMOTIVE ARTIFICIAL MARKET VALUE CHAIN
5.12 TECHNOLOGY ANALYSIS
FIGURE 31 EVOLUTION OF ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRY
FIGURE 32 ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRY
5.12.1 HUMAN–MACHINE INTERFACE
5.12.2 PREDICTIVE MAINTENANCE
5.12.3 AUTONOMOUS VEHICLE
5.12.4 ADVANCED DRIVER ASSISTANCE SYSTEM
FIGURE 33 APPLICATIONS OF ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS)
5.12.5 PRECISION AND MAPPING
5.12.6 CUSTOMER DATA ANALYSIS
5.12.7 OTHERS
5.13 KEY CONFERENCES AND EVENTS, 2022-2023
TABLE 18 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET: CONFERENCES & EVENTS (2022–2023)
5.14 KEY STAKEHOLDERS AND BUYING PROCESS AND/OR BUYING CRITERIA
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
FIGURE 34 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP 3 APPLICATIONS
TABLE 19 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP 3 APPLICATIONS (%)
5.14.2 BUYING CRITERIA
FIGURE 35 KEY BUYING CRITERIA FOR TOP 3 APPLICATIONS
TABLE 20 KEY BUYING CRITERIA FOR TOP 3 APPLICATIONS
6 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING (Page No. - 96)
6.1 INTRODUCTION
FIGURE 36 SOFTWARE SEGMENT EXPECTED TO HOLD LARGER SHARE OF AUTOMOTIVE AI MARKET IN 2022
TABLE 21 AUTOMOTIVE AI MARKET, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 22 AUTOMOTIVE AI MARKET, BY OFFERING, 2022–2027 (USD MILLION)
6.2 HARDWARE
6.2.1 VON NEUMANN ARCHITECTURE
6.2.1.1 Increasing competition among companies to provide hardware platforms
6.2.2 NEUROMORPHIC ARCHITECTURE
6.2.2.1 Designed to process information and respond to change in data
TABLE 23 AUTOMOTIVE AI HARDWARE MARKET, BY COMPUTING TYPE, 2018–2021 (USD MILLION)
TABLE 24 AUTOMOTIVE AI HARDWARE MARKET, BY COMPUTING TYPE, 2022–2027 (USD MILLION)
TABLE 25 AUTOMOTIVE AI HARDWARE MARKET, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 26 AUTOMOTIVE AI HARDWARE MARKET, BY TECHNOLOGY, 2022–2027 (USD MILLION)
FIGURE 37 NORTH AMERICA TO HOLD LARGEST MARKET SHARE OF AUTOMOTIVE AI HARDWARE MARKET DURING FORECAST PERIOD
TABLE 27 AUTOMOTIVE AI HARDWARE MARKET, BY REGION, 2018–2021 (USD MILLION)
TABLE 28 AUTOMOTIVE AI HARDWARE MARKET, BY REGION, 2022–2027 (USD MILLION)
6.3 SOFTWARE
6.3.1 SOLUTIONS
6.3.1.1 Technological development expected to boost market growth
6.3.2 PLATFORMS
6.3.2.1 Use of AI platforms to develop tool kits for various purposes
TABLE 29 AUTOMOTIVE AI SOFTWARE MARKET, BY SOFTWARE TYPE, 2018–2021 (USD MILLION)
TABLE 30 AUTOMOTIVE AI SOFTWARE MARKET, BY SOFTWARE TYPE, 2022–2027 (USD MILLION)
FIGURE 38 DEEP LEARNING TECHNOLOGY TO ACCOUNT FOR LARGEST MARKET SIZE DURING FORECAST PERIOD
TABLE 31 AUTOMOTIVE AI SOFTWARE MARKET, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 32 AUTOMOTIVE AI SOFTWARE MARKET, BY TECHNOLOGY, 2022–2027 (USD MILLION)
TABLE 33 AUTOMOTIVE AI SOFTWARE MARKET, BY REGION, 2018–2021 (USD MILLION)
TABLE 34 AUTOMOTIVE AI SOFTWARE MARKET, BY REGION, 2022–2027 (USD MILLION)
7 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY (Page No. - 106)
7.1 INTRODUCTION
FIGURE 39 DEEP LEARNING EXPECTED TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET IN 2022
TABLE 35 AUTOMOTIVE AI MARKET, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 36 AUTOMOTIVE AI MARKET, BY TECHNOLOGY, 2022–2027 (USD MILLION)
7.2 DEEP LEARNING
7.2.1 INCREASING USE IN PREDICTIVE ANALYTICS TO BOOST AUTOMOTIVE AI MARKET
TABLE 37 AUTOMOTIVE AI MARKET FOR DEEP LEARNING TECHNOLOGY, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 38 AUTOMOTIVE AI MARKET FOR DEEP LEARNING TECHNOLOGY, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 39 AUTOMOTIVE AI MARKET FOR DEEP LEARNING, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 40 AUTOMOTIVE AI MARKET FOR DEEP LEARNING, BY APPLICATION, 2022–2027 (USD MILLION)
FIGURE 40 NORTH AMERICA TO HAVE LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR DEEP LEARNING DURING FORECAST PERIOD
TABLE 41 AUTOMOTIVE AI MARKET FOR DEEP LEARNING, BY REGION, 2018–2021 (USD MILLION)
TABLE 42 AUTOMOTIVE AI MARKET FOR DEEP LEARNING, BY REGION, 2022–2027 (USD MILLION)
7.3 MACHINE LEARNING
7.3.1 MULTIPLE ADVANTAGES OF MACHINE LEARNING TO DRIVE GROWTH OF AUTOMOTIVE INDUSTRY
TABLE 43 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING TECHNOLOGY, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 44 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING TECHNOLOGY, BY OFFERING, 2022–2027 (USD MILLION)
FIGURE 41 HMI TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR MACHINE LEARNING DURING FORECAST PERIOD
TABLE 45 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 46 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 47 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING, BY REGION, 2018–2021 (USD MILLION)
TABLE 48 AUTOMOTIVE AI MARKET FOR MACHINE LEARNING, BY REGION, 2022–2027 (USD MILLION)
7.4 COMPUTER VISION
7.4.1 ABILITY TO ANALYZE INFORMATION AND PROVIDE VISUAL FEEDBACK TO DRIVE MARKET GROWTH
TABLE 49 AUTOMOTIVE AI MARKET FOR COMPUTER VISION TECHNOLOGY, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 50 AUTOMOTIVE AI MARKET FOR COMPUTER VISION TECHNOLOGY, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 51 AUTOMOTIVE AI MARKET FOR COMPUTER VISION, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 52 AUTOMOTIVE AI MARKET FOR COMPUTER VISION, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 53 AUTOMOTIVE AI MARKET FOR COMPUTER VISION, BY REGION, 2018–2021 (USD MILLION)
TABLE 54 AUTOMOTIVE AI MARKET FOR COMPUTER VISION, BY REGION, 2022–2027 (USD MILLION)
7.5 CONTEXT-AWARE COMPUTING
7.5.1 DEVELOPMENT OF SOPHISTICATED HARD AND SOFT SENSORS TO BOOST GROWTH OF SEGMENT
TABLE 55 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 56 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 57 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 58 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY APPLICATION, 2022–2027 (USD MILLION)
FIGURE 42 NORTH AMERICA TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING DURING FORECAST PERIOD
TABLE 59 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY REGION, 2018–2021 (USD MILLION)
TABLE 60 AUTOMOTIVE AI MARKET FOR CONTEXT-AWARE COMPUTING, BY REGION, 2022–2027 (USD MILLION)
7.6 NATURAL LANGUAGE PROCESSING
7.6.1 HELPS IMPROVE AUTONOMOUS VEHICLE DRIVING EXPERIENCE
FIGURE 43 SOFTWARE TO ACCOUNT FOR LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR NATURAL LANGUAGE PROCESSING DURING FORECAST PERIOD
TABLE 61 AUTOMOTIVE AI MARKET FOR NATURAL LANGUAGE PROCESSING, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 62 AUTOMOTIVE AI MARKET FOR NATURAL LANGUAGE PROCESSING, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 63 AUTOMOTIVE AI MARKET FOR NATURAL LANGUAGE PROCESSING, BY REGION, 2018–2021 (USD MILLION)
TABLE 64 AUTOMOTIVE AI MARKET FOR NATURAL LANGUAGE PROCESSING, BY REGION, 2022–2027 (USD MILLION)
8 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET, BY PROCESS (Page No. - 122)
8.1 INTRODUCTION
FIGURE 44 SIGNAL RECOGNITION SEGMENT EXPECTED TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET, BY PROCESS, IN 2022
TABLE 65 AUTOMOTIVE AI MARKET, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 66 AUTOMOTIVE AI MARKET, BY PROCESS, 2022–2027 (USD MILLION)
8.2 SIGNAL RECOGNITION
8.2.1 ENHANCED CUSTOMER EXPERIENCE AND INCREASED SAFETY
TABLE 67 AUTOMOTIVE AI MARKET FOR SIGNAL RECOGNITION, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 68 AUTOMOTIVE AI MARKET FOR SIGNAL RECOGNITION, BY APPLICATION, 2022–2027 (USD MILLION)
FIGURE 45 NORTH AMERICA TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR SIGNAL RECOGNITION DURING FORECAST PERIOD
TABLE 69 AUTOMOTIVE AI MARKET FOR SIGNAL RECOGNITION, BY REGION, 2018–2021 (USD MILLION)
TABLE 70 AUTOMOTIVE AI MARKET FOR SIGNAL RECOGNITION, BY REGION, 2022–2027 (USD MILLION)
8.3 IMAGE RECOGNITION
8.3.1 INCREASED DEMAND DUE TO ADVANCEMENTS IN AUTOMOTIVE INDUSTRY
FIGURE 46 HMI TECHNOLOGY TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET FOR IMAGE RECOGNITION DURING FORECAST PERIOD
TABLE 71 AUTOMOTIVE AI MARKET FOR IMAGE RECOGNITION, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 72 AUTOMOTIVE AI MARKET FOR IMAGE RECOGNITION, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 73 AUTOMOTIVE AI MARKET FOR IMAGE RECOGNITION, BY REGION, 2018–2021 (USD MILLION)
TABLE 74 AUTOMOTIVE AI MARKET FOR IMAGE RECOGNITION, BY REGION, 2022–2027 (USD MILLION)
8.4 DATA MINING
8.4.1 FACILITATES QUICK AND INFORMED DECISIONS
TABLE 75 AUTOMOTIVE AI MARKET FOR DATA MINING, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 76 AUTOMOTIVE AI MARKET FOR DATA MINING, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 77 AUTOMOTIVE AI MARKET, FOR DATA MINING, BY REGION, 2018–2021 (USD MILLION)
TABLE 78 AUTOMOTIVE AI MARKET, FOR DATA MINING, BY REGION, 2022–2027 (USD MILLION)
9 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET, BY APPLICATION (Page No. - 133)
9.1 INTRODUCTION
FIGURE 47 HMI APPLICATION TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET BY 2027
TABLE 79 AUTOMOTIVE AI MARKET, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 80 AUTOMOTIVE AI MARKET, BY APPLICATION, 2022–2027 (USD MILLION)
9.2 HUMAN–MACHINE INTERFACE (HMI)
9.2.1 IMPROVES CUSTOMER EXPERIENCE THROUGH INTERACTION WITH MUTLI-DASHBOARDS
FIGURE 48 NORTH AMERICA EXPECTED TO DOMINATE HMI SEGMENT OF AUTOMOTIVE AI MARKET DURING FORECAST PERIOD
TABLE 81 AUTOMOTIVE AI MARKET FOR HMI, BY REGION, 2018–2021 (USD MILLION)
TABLE 82 AUTOMOTIVE AI MARKET FOR HMI, BY REGION, 2022–2027 (USD MILLION)
TABLE 83 AUTOMOTIVE AI MARKET FOR HMI, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 84 AUTOMOTIVE AI MARKET FOR HMI, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 85 AUTOMOTIVE AI MARKET FOR HMI, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 86 AUTOMOTIVE AI MARKET FOR HMI, BY TECHNOLOGY, 2022–2027 (USD MILLION)
9.3 SEMI-AUTONOMOUS DRIVING
9.3.1 USE OF AI TO CONTROL SPECIFIC ENVIRONMENTAL CONDITIONS
TABLE 87 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY REGION, 2018–2021 (USD MILLION)
TABLE 88 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY REGION, 2022–2027 (USD MILLION)
FIGURE 49 SIGNAL RECOGNITION EXPECTED TO DOMINATE AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING APPLICATION DURING FORECAST PERIOD
TABLE 89 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 90 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 91 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 92 AUTOMOTIVE AI MARKET FOR SEMI-AUTONOMOUS DRIVING, BY TECHNOLOGY, 2022–2027 (USD MILLION)
9.4 AUTONOMOUS DRIVING
9.4.1 USE OF DEEP LEARNING AND SENSOR FUSION TECHNOLOGY FOR SELF-DRIVING CARS
TABLE 93 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY REGION, 2018–2021 (USD MILLION)
TABLE 94 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY REGION, 2022–2027 (USD MILLION)
TABLE 95 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 96 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY PROCESS, 2022–2027 (USD MILLION)
FIGURE 50 DEEP LEARNING EXPECTED TO DOMINATE AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING APPLICATION DURING FORECAST PERIOD
TABLE 97 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 98 AUTOMOTIVE AI MARKET FOR AUTONOMOUS DRIVING, BY TECHNOLOGY, 2022–2027 (USD MILLION)
9.5 IDENTITY AUTHENTICATION
9.5.1 LIMITING ACCESS TO AUTHORIZED USERS FOR IMPROVED SAFETY
9.6 DRIVER MONITORING
9.6.1 DRIVER MONITORING SYSTEMS REDUCE RISK OF COLLISIONS
9.7 AUTONOMOUS DRIVING PROCESSOR CHIPS
9.7.1 GROWING DEMAND FOR HIGH-PERFORMANCE DATA PROCESSING CHIPS
10 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET, BY COMPONENT (Page No. - 146)
10.1 INTRODUCTION
FIGURE 51 GPU COMPONENT TO HOLD LARGEST SHARE OF AUTOMOTIVE AI MARKET BY 2027
TABLE 99 AUTOMOTIVE AI MARKET, BY COMPONENT, 2018–2021 (USD MILLION)
TABLE 100 AUTOMOTIVE AI MARKET, BY COMPONENT, 2022–2027 (USD MILLION)
10.2 MICROPROCESSORS
10.2.1 DESIGNED TO BE CRUCIAL COMPONENTS OF VEHICLES
10.3 GRAPHICS PROCESSING UNIT (GPU)
10.3.1 USED TO SUPPORT DIGITAL DASHBOARDS IN VEHICLES
10.4 FIELD PROGRAMMABLE GATE ARRAY (FPGA)
10.4.1 ENABLE INCREASED CUSTOMIZATION AND SCALABILITY
10.5 MEMORY AND STORAGE SYSTEMS
10.5.1 RISING HIGH-BANDWIDTH MEMORY REQUIREMENTS TO DRIVE MARKET GROWTH
10.6 IMAGE SENSORS
10.6.1 USED TO ENHANCE PERFORMANCE OF AUTONOMOUS VEHICLES
10.7 BIOMETRIC SCANNERS
10.7.1 VARIED APPLICATIONS LEAD TO HIGH ADOPTION
10.8 OTHERS
10.8.1 CLOUD AI FACILITATES TIMELY AVAILABILITY OF DATA
11 GEOGRAPHIC ANALYSIS (Page No. - 152)
11.1 INTRODUCTION
FIGURE 52 NORTH AMERICA EXPECTED TO DOMINATE AUTOMOTIVE AI MARKET DURING FORECAST PERIOD
TABLE 101 AUTOMOTIVE AI MARKET, BY REGION, 2018–2021 (USD MILLION)
TABLE 102 AUTOMOTIVE AI MARKET, BY REGION, 2022–2027 (USD MILLION)
11.2 NORTH AMERICA
FIGURE 53 NORTH AMERICA: MARKET SNAPSHOT
TABLE 103 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 104 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 105 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 106 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 107 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 108 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 109 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 110 AUTOMOTIVE AI MARKET, FOR NORTH AMERICA, BY TECHNOLOGY, 2022–2027 (USD MILLION)
TABLE 111 AUTOMOTIVE AI MARKET, NORTH AMERICA, BY COUNTRY, 2018–2021 (USD MILLION)
TABLE 112 AUTOMOTIVE AI MARKET, NORTH AMERICA, BY COUNTRY, 2022–2027 (USD MILLION)
11.2.1 US
11.2.1.1 Development of autonomous vehicles expected to propel demand for automotive AI
11.2.2 CANADA
11.2.2.1 Increasing adoption of AI-based systems in automotive industry
11.2.3 MEXICO
11.2.3.1 Low labor costs to boost automotive AI market
11.3 EUROPE
FIGURE 54 EUROPE: MARKET SNAPSHOT
TABLE 113 AUTOMOTIVE AI MARKET, FOR EUROPE, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 114 AUTOMOTIVE AI MARKET, FOR EUROPE, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 115 AUTOMOTIVE AI MARKET, FOR EUROPE, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 116 AUTOMOTIVE AI MARKET, FOR EUROPE, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 117 AUTOMOTIVE AI MARKET, FOR EUROPE, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 118 AUTOMOTIVE AI MARKET, FOR EUROPE, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 119 AUTOMOTIVE AI MARKET, FOR EUROPE, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 120 AUTOMOTIVE AI MARKET, FOR EUROPE, BY TECHNOLOGY, 2022–2027 (USD MILLION)
FIGURE 55 GERMANY EXPECTED TO DOMINATE AUTOMOTIVE AI MARKET IN EUROPE DURING FORECAST PERIOD
TABLE 121 AUTOMOTIVE AI MARKET, EUROPE, BY REGION, 2018–2021 (USD MILLION)
TABLE 122 AUTOMOTIVE AI MARKET, EUROPE, BY REGION, 2022–2027 (USD MILLION)
11.3.1 GERMANY
11.3.1.1 High adoption of computer vision systems to propel market growth
11.3.2 FRANCE
11.3.2.1 increasing demand for advanced vehicle safety features to boost market
11.3.3 UK
11.3.3.1 Increasing demand for AI-based solutions to drive market
11.3.4 REST OF EUROPE
11.3.4.1 Increasing AI development to boost opportunities for market growth
11.4 ASIA PACIFIC
FIGURE 56 ASIA PACIFIC: MARKET SNAPSHOT
TABLE 123 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 124 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 125 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 126 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 127 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 128 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 129 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 130 AUTOMOTIVE AI MARKET, FOR ASIA PACIFIC, BY TECHNOLOGY, 2022–2027 (USD MILLION)
FIGURE 57 CHINA EXPECTED TO DOMINATE AUTOMOTIVE AI MARKET IN ASIA PACIFIC DURING FORECAST PERIOD
TABLE 131 AUTOMOTIVE AI MARKET, ASIA PACIFIC, BY REGION, 2018–2021 (USD MILLION))
TABLE 132 AUTOMOTIVE AI MARKET, ASIA PACIFIC, BY REGION, 2022–2027 (USD MILLION))
11.4.1 CHINA
11.4.1.1 Growth of automotive sector to drive market growth
11.4.2 JAPAN
11.4.2.1 Presence of numerous AI solution providers to bolster market growth
11.4.3 SOUTH KOREA
11.4.3.1 Government regulations to boost market
11.4.4 REST OF APAC
11.4.4.1 Rising use of driver assistance systems to support market
11.5 REST OF THE WORLD
TABLE 133 AUTOMOTIVE AI MARKET, FOR ROW, BY APPLICATION, 2018–2021 (USD MILLION)
TABLE 134 AUTOMOTIVE AI MARKET, FOR ROW, BY APPLICATION, 2022–2027 (USD MILLION)
TABLE 135 AUTOMOTIVE AI MARKET, FOR ROW, BY OFFERING, 2018–2021 (USD MILLION)
TABLE 136 AUTOMOTIVE AI MARKET, FOR ROW, BY OFFERING, 2022–2027 (USD MILLION)
TABLE 137 AUTOMOTIVE AI MARKET, FOR ROW, BY PROCESS, 2018–2021 (USD MILLION)
TABLE 138 AUTOMOTIVE AI MARKET, FOR ROW, BY PROCESS, 2022–2027 (USD MILLION)
TABLE 139 AUTOMOTIVE AI MARKET, FOR ROW, BY TECHNOLOGY, 2018–2021 (USD MILLION)
TABLE 140 AUTOMOTIVE AI MARKET, FOR ROW, BY TECHNOLOGY, 2022–2027 (USD MILLION)
TABLE 141 AUTOMOTIVE AI MARKET, ROW, BY REGION, 2018–2021 (USD MILLION)
TABLE 142 AUTOMOTIVE AI MARKET, ROW, BY REGION, 2022–2027 (USD MILLION)
11.5.1 SOUTH AMERICA
11.5.1.1 Development of AI technologies expected to boost market growth
11.5.2 MIDDLE EAST & AFRICA
11.5.2.1 Increasing investments to bolster market
12 COMPETITIVE LANDSCAPE (Page No. - 177)
12.1 INTRODUCTION
12.2 REVENUE ANALYSIS: TOP COMPANIES
FIGURE 58 TOP 5 PLAYERS DOMINATED AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET OVER LAST 5 YEARS
12.3 STRATEGIES ADOPTED BY KEY PLAYERS
TABLE 143 OVERVIEW OF STRATEGIES ADOPTED BY PLAYERS IN MARKET
12.4 MARKET SHARE ANALYSIS, 2021
TABLE 144 MARKET: DEGREE OF COMPETITION
FIGURE 59 MARKET SHARE, BY COMPANY (2021)
12.5 COMPANY EVALUATION QUADRANT
12.5.1 STARS
12.5.2 PERVASIVE PLAYERS
12.5.3 EMERGING LEADERS
12.5.4 PARTICIPANTS
FIGURE 60 AUTOMOTIVE ARTIFICIAL INTELLIGENCE MARKET (GLOBAL) COMPANY EVALUATION QUADRANT, 2021
12.6 COMPANY FOOTPRINT
12.6.1 APPLICATION AND REGIONAL FOOTPRINT OF TOP PLAYERS
TABLE 145 APPLICATION AND REGIONAL FOOTPRINT OF TOP COMPANIES
TABLE 146 APPLICATION FOOTPRINT OF COMPANIES
TABLE 147 REGIONAL FOOTPRINT OF COMPANIES
12.7 START-UP/SME EVALUATION QUADRANT, 2021
12.7.1 PROGRESSIVE COMPANIES
12.7.2 RESPONSIVE COMPANIES
12.7.3 DYNAMIC COMPANIES
12.7.4 STARTING BLOCKS
FIGURE 61 MARKET (GLOBAL) START-UP/SME EVALUATION QUADRANT, 2021
12.8 COMPETITIVE SCENARIOS & TRENDS
TABLE 148 AUTOMOTIVE ARTIFICIAL INTELLIGENT MARKET: PRODUCT LAUNCHES
TABLE 149 MARKET: DEALS
12.9 COMPETITIVE BENCHMARKING
TABLE 150 MARKET: KEY START-UPS/SMES
TABLE 151 MARKET: COMPETITIVE BENCHMARKING OF KEY START-UPS/SMES
13 COMPANY PROFILES (Page No. - 195)
13.1 KEY PLAYERS
(Business Overview, Products Offered, Recent Developments, and MnM View)*
13.1.1 NVIDIA CORPORATION
TABLE 152 NVIDIA: BUSINESS OVERVIEW
FIGURE 62 NVIDIA CORPORATION: COMPANY SNAPSHOT
TABLE 153 NVIDIA: PRODUCTS OFFERED
TABLE 154 NVIDIA: DEALS
13.1.2 ALPHABET INC.
TABLE 155 ALPHABET: BUSINESS OVERVIEW
FIGURE 63 ALPHABET INC.: COMPANY SNAPSHOT
TABLE 156 ALPHABET: PRODUCTS OFFERED
TABLE 157 ALPHABET: PRODUCT LAUNCHES
TABLE 158 ALPHABET: DEALS
13.1.3 INTEL CORPORATION
TABLE 159 INTEL: BUSINESS OVERVIEW
FIGURE 64 INTEL CORPORATION: COMPANY SNAPSHOT
TABLE 160 INTEL: PRODUCTS OFFERED
TABLE 161 INTEL: PRODUCT LAUNCHES
TABLE 162 INTEL: DEALS
13.1.4 MICROSOFT CORPORATION
TABLE 163 MICROSOFT CORPORATION: BUSINESS OVERVIEW
FIGURE 65 MICROSOFT CORPORATION: COMPANY SNAPSHOT
TABLE 164 MICROSOFT CORPORATION: PRODUCTS OFFERED
TABLE 165 MICROSOFT CORPORATION: PRODUCT LAUNCHES
TABLE 166 MICROSOFT CORPORATION: DEALS
13.1.5 INTERNATIONAL BUSINESS MACHINES CORPORATION
TABLE 167 IBM: BUSINESS OVERVIEW
FIGURE 66 IBM CORPORATION: COMPANY SNAPSHOT
TABLE 168 IBM: PRODUCTS OFFERED
TABLE 169 IBM: PRODUCT LAUNCHES
13.1.6 QUALCOMM INC.
TABLE 170 QUALCOMM: BUSINESS OVERVIEW
FIGURE 67 QUALCOMM INC.: COMPANY SNAPSHOT
TABLE 171 QUALCOMM: PRODUCTS OFFERED
TABLE 172 QUALCOMM: PRODUCT LAUNCHES
TABLE 173 QUALCOMM: DEALS
13.1.7 TESLA, INC.
TABLE 174 TESLA: BUSINESS OVERVIEW
FIGURE 68 TESLA INC.: COMPANY SNAPSHOT
TABLE 175 TESLA: PRODUCTS OFFERED
TABLE 176 TESLA: DEALS
13.1.8 BAYERISCHE MOTOREN WERKE AG
TABLE 177 BMW AG: BUSINESS OVERVIEW
FIGURE 69 BAYERISCHE MOTOREN WERKE AG: COMPANY SNAPSHOT
TABLE 178 BMW AG: PRODUCTS OFFERED
TABLE 179 BMW AG: PRODUCT LAUNCHES
TABLE 180 BMW AG: DEALS
13.1.9 XILINX, INC.
TABLE 181 XILINX, INC.: BUSINESS OVERVIEW
FIGURE 70 XILINX, INC.: COMPANY SNAPSHOT
TABLE 182 XILINX, INC.: PRODUCTS OFFERED
TABLE 183 XILINX, INC.: PRODUCT LAUNCHES
TABLE 184 XILINX, INC.: DEALS
13.1.10 MICRON TECHNOLOGY
TABLE 185 MICRON TECHNOLOGY: BUSINESS OVERVIEW
FIGURE 71 MICRON TECHNOLOGY: COMPANY SNAPSHOT
TABLE 186 MICRON TECHNOLOGY: PRODUCTS OFFERED
TABLE 187 MICRON TECHNOLOGY: PRODUCT LAUNCHES
TABLE 188 MICRON TECHNOLOGY: DEALS
13.2 OTHER KEY PLAYERS
13.2.1 HARMAN INTERNATIONAL INDUSTRIES, INC.
13.2.2 VOLVO CARS
13.2.3 AUDI AG
13.2.4 GENERAL MOTORS COMPANY
13.2.5 FORD MOTOR COMPANY
13.2.6 TOYOTA MOTOR CORPORATION
13.2.7 HONDA MOTOR CO. LTD.
13.2.8 HYUNDAI MOTOR CO., LTD
13.2.9 DAIMLER AG
13.2.10 UBER TECHNOLOGIES, INC
13.2.11 DIDI CHUXING
13.3 OTHER PLAYERS
13.3.1 MITSUBISHI ELECTRIC
13.3.2 AUTOMOTIVE ARTIFICIAL INTELLIGENCE (AAI) GMBH
13.3.3 NAUTO
13.3.4 ARGO AI
13.3.5 GERMAN AUTOLABS
13.3.6 TRACTABLE
13.3.7 IGLOBLE
13.3.8 SONICLUE
13.3.9 ATHER
13.3.10 RIVIGO
13.3.11 MOTIONAL
13.3.12 REFRACTION AI
13.3.13 SAPIENTX
13.3.14 CARVI
13.3.15 ZOOX
* Business Overview, Products Offered, Recent Developments, and MnM View might not be captured in case of unlisted companies.
14 APPENDIX (Page No. - 260)
14.1 INSIGHTS FROM INDUSTRY EXPERTS
14.2 DISCUSSION GUIDE
14.3 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.4 CUSTOMIZATION OPTIONS
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
The study involved four major activities in estimating the size of the automotive artificial intelligence market. Exhaustive secondary research has been done to collect information on the market, peer market, and parent market. Validation of these findings, assumptions, and sizing with industry experts across the value chain through primary research has been the next step. Both top-down and bottom-up approaches have been employed to estimate the global market size. After that, market breakdown and data triangulation have been used to estimate the market sizes of segments and subsegments.
Secondary Research
The secondary sources referred to for this research study includes corporate filings (such as annual reports, press releases, investor presentations, and financial statements); trade, business, and professional associations (such as Consumer Technology Association (CTA), Integrated Systems Europe, the Organisation Internationale des Constructeurs d'Automobiles (OICA), the Society for Information Display (SID), and Touch Taiwan); white papers, certified publications, and articles by recognized authors; gold and silver standard websites; directories; and databases.
Secondary research has been conducted to obtain key information about the supply chain of the automotive artificial intelligence industry, the monetary chain of the market, the total pool of key players, and market segmentation according to the industry trends to the bottommost level, regional markets, and key developments from both market- and technology oriented perspectives. The secondary data has been collected and analyzed to arrive at the overall market size, which has further been validated by primary research.
Primary Research
Extensive primary research has been conducted after acquiring an understanding of the automotive artificial intelligence market scenario through secondary research. Several primary interviews have been conducted with market experts from both the demand- (consumers, industries) and supply-side (automotive artificial intelligence providers) players across four major regions, namely, North America, Europe, Asia Pacific, and the Rest of the World (the Middle East & Africa). Approximately 75% and 25% of primary interviews have been conducted from the supply and demand side, respectively. Primary data has been collected through questionnaires, emails, and telephonic interviews. In the canvassing of primaries, various departments within organizations, such as sales, operations, and administration, were covered to provide a holistic viewpoint in our report.
After interacting with industry experts, brief sessions were conducted with highly experienced independent consultants to reinforce the findings from our primaries. This, along with the in-house subject matter experts’ opinions, has led us to the findings as described in the remainder of this report.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
Both top-down and bottom-up approaches have been used to estimate and validate the total size of the automotive artificial intelligence market. These methods have also been extensively used to estimate the sizes of various market subsegments. The research methodology used to estimate the market sizes includes the following:
- Identifying various applications that use or are expected to use the automotive artificial intelligence market.
- Analyzing historical and current data pertaining to the size of the automotive artificial intelligence market, in terms of volume, for each application using their production statistics
- Analyzing the average selling prices of automotive artificial intelligence based on different technologies
- Studying various paid and unpaid sources, such as annual reports, press releases, white papers, and databases
- Identifying leading manufacturers of automotive artificial intelligence sensors, studying their portfolios, and understanding features of their products and their underlying technologies, as well as the types of automotive artificial intelligence products offered
- Tracking ongoing and identifying upcoming developments in the market through investments, research and development activities, product launches, expansions, and partnerships, and forecasting the market size based on these developments and other critical parameters
- Carrying out multiple discussions with key opinion leaders to understand the technologies used in automotive artificial intelligence, raw materials used to develop them, and products wherein they are deployed, and analyze the break-up of the scope of work carried out by key manufacturers of automotive artificial intelligence solutions providers
- Verifying and crosschecking estimates at every level through discussions with key opinion leaders, such as CXOs, directors, and operations managers, and finally with domain experts at MarketsandMarkets
Market Size Estimation Methodology-Bottom-up approach
Data Triangulation
After arriving at the overall market size-using the market size estimation processes explained above-the market has been split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation, and market breakdown procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides.
The main objectives of this study are as follows:
- To define, describe, segment, and forecast the automotive artificial intelligence market, in terms of value, based on offerings, technology, process, application, components, and region.
- To forecast the automotive artificial intelligence market, in terms of volume, based on application
- To forecast the size of the market and its segments with respect to four main regions, namely, North America, Europe, Asia Pacific (APAC), and the Rest of the World (RoW), along with their key countries
- To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contributions to the total market
- To provide detailed information regarding the key factors influencing market growth, such as drivers, restraints, opportunities, and challenges
- To provide a detailed analysis of the automotive artificial intelligence supply chain
- To analyze the opportunities in the market for stakeholders and provide a detailed competitive landscape of the market leaders
- To strategically profile the key players and comprehensively analyze their market ranking and core competencies2
- To analyze key growth strategies such as expansions, contracts, joint ventures, acquisitions, product launches and developments, and research and development activities undertaken by players operating in the automotive artificial intelligence market.
Available Customizations:
MarketsandMarkets offers the following customizations for this market report:
- Further breakdown of the market in different regions to the country-level
- Detailed analysis and profiling of additional market players (up to 5)
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Growth opportunities and latent adjacency in Automotive Artificial Intelligence (AI) Market