The AI in Autonomous Vehicles Market Size is valued at USD 4.4 billion in 2023 and is predicted to reach USD 17.9 billion by the year 2031 at a 19.2% CAGR during the forecast period for 2024-2031.
AI in Autonomous Vehicles refers to the implementation of AI technology in vehicles to improve several aspects of the automobile system, including efficiency, safety, and convenience, and to improve the overall vehicle driving experience. The automotive industry has witnessed the potential of Al and is among the major industries utilizing Al technologies to augment and mimic human actions.
Furthermore, the emergence of modern automobile functions such as advanced driver assistance system (ADAS), blind spot alert, adaptive cruise control (ACC), autonomous driving, predictive maintenance, intelligent traffic management, and growth in demand for convenience features attract automotive manufacturers toward implementation of Al in automobiles. The expansion of the automotive artificial intelligence market is driven by an increase in demand for autonomous vehicles, growth in high-speed internet & 5G technology, and a rise in need for enhanced user experience & convenient features. However, a rise in security and privacy concerns and a stringent regulatory landscape are anticipated to hinder the market growth.
Furthermore, the increase in demand for premium vehicles and growth in connected vehicle technology are anticipated to deliver lucrative growth opportunities for the global market during the forecast period. In recent years, autonomous vehicles have gained popularity due to various features such as automatic parking, self-driving, autopilot, and others. Autonomous vehicles minimize human effort while driving.
The AI in Autonomous Vehicles market is segmented as type, application, component, and technology. As per the type segment, the market is further segmented into software, hardware, and services. By application, the market is segmented into Semi-autonomous Vehicles and fully Autonomous Vehicles. According to the components, the market is segmented into artificial intelligence (AI) processors, sensors, software, cameras, LiDAR, radar, GPS navigation systems, and others. As per the technology, the market is categorized into deep learning, natural language processing (NLP), context awareness, machine learning, predictive analytics, computer vision, and others.
The software category is expected to lead with a major share of the global AI in the Autonomous vehicles market. Software solutions are crucial for the operation of autonomous vehicles, encompassing various functionalities such as machine learning algorithms, data analytics, and real-time decision-making capabilities. These software systems enable the vehicles to interpret and respond to their surroundings accurately, enhancing safety and efficiency. With continuous innovations and improvements in AI software, the reliability and performance of autonomous vehicles are expected to advance, leading to broader adoption. Additionally, the increasing integration of AI software in vehicles for navigation, obstacle detection, and predictive maintenance further underscores its importance in the market.
Semi-autonomous vehicles, which incorporate advanced driver assistance systems (ADAS) like adaptive cruise control, lane-keeping assistance, and automated parking, are increasingly favoured due to their blend of automation and driver control. The rising demand for enhanced safety, convenience, and driving experience, coupled with stringent government regulations aimed at reducing road accidents, is propelling the growth of this segment.
The North American AI in Autonomous Vehicles market holds a significant revenue share due to the region's advanced technological infrastructure, robust automotive industry, and high investment in R&D activities. Major automotive manufacturers and tech companies are headquartered in this region, driving innovation and early adoption of AI technologies in autonomous vehicles. Additionally, supportive government regulations and initiatives, such as funding for smart transportation systems and favourable policies for testing autonomous vehicles on public roads, further propel market growth. The presence of key players and partnerships between automotive and technology firms also contribute to the market's expansion.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 4.4 Bn |
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Revenue Forecast In 2031 |
USD 17.9 Bn |
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Growth Rate CAGR |
CAGR of 19.2% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, Component, And Technology |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Nvidia Corporation, Alphabet Inc., Intel Corporation, Microsoft Corporation, IBM Corporation, Qualcomm Inc., Tesla Inc., BMW AG, Micron Technology, Xilinx Inc., Harman International Industries Inc., Volvo Car Corporation, Audi AG, General Motors Company, Ford Motor Company, Motor Corporation, Honda Motor Co. Ltd., Hyundai Motor Corporation, Daimler AG, Uber Technologies Inc., Didi Chuxing, Mitsubishi Electric, Automotive Artificial Intelligence (AAI) GmbH, and Others |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Autonomous Vehicles Market Snapshot
Chapter 4. Global AI in Autonomous Vehicles Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2023 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
5.2.1. Software
5.2.2. Hardware
5.2.3. Services
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2023 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:
6.2.1. Semi-autonomous Vehicles
6.2.2. Fully Autonomous Vehicles
Chapter 7. Market Segmentation 3: by Component Estimates & Trend Analysis
7.1. by Component & Market Share, 2023 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Component:
7.2.1. Artificial Intelligence (AI) Processors
7.2.2. Sensors
7.2.3. Software
7.2.4. Cameras
7.2.5. LiDAR
7.2.6. Radar
7.2.7. GPS Navigation System
7.2.8. Others
Chapter 8. Market Segmentation 4: by Technology Estimates & Trend Analysis
8.1. By Technology & Market Share, 2023 & 2031
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following By Technology:
8.2.1. Deep Learning
8.2.2. Computer Vision
8.2.3. Natural Language Processing (NLP)
8.2.4. Context Awareness
8.2.5. Machine Learning
8.2.6. Predictive Analytics
8.2.7. Others
Chapter 9. AI in Autonomous Vehicles Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. North America
9.1.1. North America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.1.2. North America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.1.3. North America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
9.1.4. North America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2024-2031
9.1.5. North America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
9.2. Europe
9.2.1. Europe AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.2.2. Europe AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.2.3. Europe AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
9.2.4. Europe AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2024-2031
9.2.5. Europe AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
9.3. Asia Pacific
9.3.1. Asia Pacific AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.3.2. Asia Pacific AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.3.3. Asia-Pacific Thermal Cyclers Asia-Pacific AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
9.3.4. Asia-Pacific AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2024-2031
9.3.5. Asia Pacific AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
9.4. Latin America
9.4.1. Latin America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.4.2. Latin America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.4.3. Latin America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
9.4.4. Latin America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2024-2031
9.4.5. Latin America AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
9.5. Middle East & Africa
9.5.1. Middle East & Africa AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.5.2. Middle East & Africa AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.5.3. Middle East & Africa AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
9.5.4. Middle East & Africa AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2024-2031
9.5.5. Middle East & Africa AI in Autonomous Vehicles Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles
10.2.1. Nvidia Corporation
10.2.2. Alphabet Inc.
10.2.3. Intel Corporation
10.2.4. Microsoft Corporation
10.2.5. IBM Corporation
10.2.6. Qualcomm Inc.
10.2.7. Tesla Inc.
10.2.8. BMW AG
10.2.9. Micron Technology
10.2.10. Xilinx Inc.
10.2.11. Harman International Industries Inc.
10.2.12. Volvo Car Corporation
10.2.13. Audi AG
10.2.14. General Motors Company
10.2.15. Ford Motor Company
10.2.16. Motor Corporation
10.2.17. Honda Motor Co. Ltd.
10.2.18. Hyundai Motor Corporation
10.2.19. Daimler AG
10.2.20. Uber Technologies Inc.
10.2.21. Didi Chuxing
10.2.22. Mitsubishi Electric
10.2.23. Automotive Artificial Intelligence (AAI) GmbH
10.2.24. Other Market Players
AI in Autonomous Vehicles Market By Type-
AI in Autonomous Vehicles Market By Application-
AI in Autonomous Vehicles Market By Component-
AI in Autonomous Vehicles Market By Technology-
AI in Autonomous Vehicles Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
