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Global Machine Learning in Automobile Market Research Report 2024
Synopsis
Machine learning in the automotive industry has a remarkable ability to bring out hidden relationships among data sets and make predictions.
The global Machine Learning in Automobile market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
Automotive is a key driver of this industry. According to data from the World Automobile Organization (OICA), global automobile production and sales in 2017 reached their peak in the past 10 years, at 97.3 million and 95.89 million respectively. In 2018, the global economic expansion ended, and the global auto market declined as a whole. In 2022, there will wear units 81.6 million vehicles in the world. At present, more than 90% of the world's automobiles are concentrated in the three continents of Asia, Europe and North America, of which Asia automobile production accounts for 56% of the world, Europe accounts for 20%, and North America accounts for 16%. The world major automobile producing countries include China, the United States, Japan, South Korea, Germany, India, Mexico, and other countries; among them, China is the largest automobile producing country in the world, accounting for about 32%. Japan is the world's largest car exporter, exporting more than 3.5 million vehicles in 2022.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Automobile, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Automobile.
Report Scope
The Machine Learning in Automobile market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Automobile market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Automobile companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Allerin
Intellias Ltd
NVIDIA Corporation
Xevo
Kopernikus Automotive
Blippar
Alphabet Inc
Intel
IBM
Microsoft
Segment by Type
Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforced Leaning
Segment by Application
AI Cloud Services
Automotive Insurance
Car Manufacturing
Driver Monitoring
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Automobile companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Machine learning in the automotive industry has a remarkable ability to bring out hidden relationships among data sets and make predictions.
The global Machine Learning in Automobile market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
Automotive is a key driver of this industry. According to data from the World Automobile Organization (OICA), global automobile production and sales in 2017 reached their peak in the past 10 years, at 97.3 million and 95.89 million respectively. In 2018, the global economic expansion ended, and the global auto market declined as a whole. In 2022, there will wear units 81.6 million vehicles in the world. At present, more than 90% of the world's automobiles are concentrated in the three continents of Asia, Europe and North America, of which Asia automobile production accounts for 56% of the world, Europe accounts for 20%, and North America accounts for 16%. The world major automobile producing countries include China, the United States, Japan, South Korea, Germany, India, Mexico, and other countries; among them, China is the largest automobile producing country in the world, accounting for about 32%. Japan is the world's largest car exporter, exporting more than 3.5 million vehicles in 2022.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Automobile, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Automobile.
Report Scope
The Machine Learning in Automobile market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Automobile market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Automobile companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Allerin
Intellias Ltd
NVIDIA Corporation
Xevo
Kopernikus Automotive
Blippar
Alphabet Inc
Intel
IBM
Microsoft
Segment by Type
Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforced Leaning
Segment by Application
AI Cloud Services
Automotive Insurance
Car Manufacturing
Driver Monitoring
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Automobile companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Machine Learning in Automobile Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Supervised Learning
1.2.3 Unsupervised Learning
1.2.4 Semi Supervised Learning
1.2.5 Reinforced Leaning
1.3 Market by Application
1.3.1 Global Machine Learning in Automobile Market Growth by Application: 2019 VS 2023 VS 2030
1.3.2 AI Cloud Services
1.3.3 Automotive Insurance
1.3.4 Car Manufacturing
1.3.5 Driver Monitoring
1.3.6 Others
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning in Automobile Market Perspective (2019-2030)
2.2 Machine Learning in Automobile Growth Trends by Region
2.2.1 Global Machine Learning in Automobile Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning in Automobile Historic Market Size by Region (2019-2024)
2.2.3 Machine Learning in Automobile Forecasted Market Size by Region (2025-2030)
2.3 Machine Learning in Automobile Market Dynamics
2.3.1 Machine Learning in Automobile Industry Trends
2.3.2 Machine Learning in Automobile Market Drivers
2.3.3 Machine Learning in Automobile Market Challenges
2.3.4 Machine Learning in Automobile Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning in Automobile Players by Revenue
3.1.1 Global Top Machine Learning in Automobile Players by Revenue (2019-2024)
3.1.2 Global Machine Learning in Automobile Revenue Market Share by Players (2019-2024)
3.2 Global Machine Learning in Automobile Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning in Automobile Revenue
3.4 Global Machine Learning in Automobile Market Concentration Ratio
3.4.1 Global Machine Learning in Automobile Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning in Automobile Revenue in 2023
3.5 Machine Learning in Automobile Key Players Head office and Area Served
3.6 Key Players Machine Learning in Automobile Product Solution and Service
3.7 Date of Enter into Machine Learning in Automobile Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning in Automobile Breakdown Data by Type
4.1 Global Machine Learning in Automobile Historic Market Size by Type (2019-2024)
4.2 Global Machine Learning in Automobile Forecasted Market Size by Type (2025-2030)
5 Machine Learning in Automobile Breakdown Data by Application
5.1 Global Machine Learning in Automobile Historic Market Size by Application (2019-2024)
5.2 Global Machine Learning in Automobile Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning in Automobile Market Size (2019-2030)
6.2 North America Machine Learning in Automobile Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning in Automobile Market Size by Country (2019-2024)
6.4 North America Machine Learning in Automobile Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning in Automobile Market Size (2019-2030)
7.2 Europe Machine Learning in Automobile Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning in Automobile Market Size by Country (2019-2024)
7.4 Europe Machine Learning in Automobile Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning in Automobile Market Size (2019-2030)
8.2 Asia-Pacific Machine Learning in Automobile Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning in Automobile Market Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning in Automobile Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning in Automobile Market Size (2019-2030)
9.2 Latin America Machine Learning in Automobile Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning in Automobile Market Size by Country (2019-2024)
9.4 Latin America Machine Learning in Automobile Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning in Automobile Market Size (2019-2030)
10.2 Middle East & Africa Machine Learning in Automobile Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning in Automobile Market Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning in Automobile Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Allerin
11.1.1 Allerin Company Detail
11.1.2 Allerin Business Overview
11.1.3 Allerin Machine Learning in Automobile Introduction
11.1.4 Allerin Revenue in Machine Learning in Automobile Business (2019-2024)
11.1.5 Allerin Recent Development
11.2 Intellias Ltd
11.2.1 Intellias Ltd Company Detail
11.2.2 Intellias Ltd Business Overview
11.2.3 Intellias Ltd Machine Learning in Automobile Introduction
11.2.4 Intellias Ltd Revenue in Machine Learning in Automobile Business (2019-2024)
11.2.5 Intellias Ltd Recent Development
11.3 NVIDIA Corporation
11.3.1 NVIDIA Corporation Company Detail
11.3.2 NVIDIA Corporation Business Overview
11.3.3 NVIDIA Corporation Machine Learning in Automobile Introduction
11.3.4 NVIDIA Corporation Revenue in Machine Learning in Automobile Business (2019-2024)
11.3.5 NVIDIA Corporation Recent Development
11.4 Xevo
11.4.1 Xevo Company Detail
11.4.2 Xevo Business Overview
11.4.3 Xevo Machine Learning in Automobile Introduction
11.4.4 Xevo Revenue in Machine Learning in Automobile Business (2019-2024)
11.4.5 Xevo Recent Development
11.5 Kopernikus Automotive
11.5.1 Kopernikus Automotive Company Detail
11.5.2 Kopernikus Automotive Business Overview
11.5.3 Kopernikus Automotive Machine Learning in Automobile Introduction
11.5.4 Kopernikus Automotive Revenue in Machine Learning in Automobile Business (2019-2024)
11.5.5 Kopernikus Automotive Recent Development
11.6 Blippar
11.6.1 Blippar Company Detail
11.6.2 Blippar Business Overview
11.6.3 Blippar Machine Learning in Automobile Introduction
11.6.4 Blippar Revenue in Machine Learning in Automobile Business (2019-2024)
11.6.5 Blippar Recent Development
11.7 Alphabet Inc
11.7.1 Alphabet Inc Company Detail
11.7.2 Alphabet Inc Business Overview
11.7.3 Alphabet Inc Machine Learning in Automobile Introduction
11.7.4 Alphabet Inc Revenue in Machine Learning in Automobile Business (2019-2024)
11.7.5 Alphabet Inc Recent Development
11.8 Intel
11.8.1 Intel Company Detail
11.8.2 Intel Business Overview
11.8.3 Intel Machine Learning in Automobile Introduction
11.8.4 Intel Revenue in Machine Learning in Automobile Business (2019-2024)
11.8.5 Intel Recent Development
11.9 IBM
11.9.1 IBM Company Detail
11.9.2 IBM Business Overview
11.9.3 IBM Machine Learning in Automobile Introduction
11.9.4 IBM Revenue in Machine Learning in Automobile Business (2019-2024)
11.9.5 IBM Recent Development
11.10 Microsoft
11.10.1 Microsoft Company Detail
11.10.2 Microsoft Business Overview
11.10.3 Microsoft Machine Learning in Automobile Introduction
11.10.4 Microsoft Revenue in Machine Learning in Automobile Business (2019-2024)
11.10.5 Microsoft Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
Published By : QY Research
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