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.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 4.4 Bn |
| Revenue Forecast In 2031 | USD 17.9 Bn |
| Growth Rate CAGR | CAGR of 19.2% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, Application, Component, And Technology |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| 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 |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
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-
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This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.