Authors:
Suraj Gujar, Ankita Chavan
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Automotive AI Chipset Market Size & Share 2023 to 2032
Report ID: GMI6794
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Published Date: September 2023
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Automotive AI Chipset Market Size
Automotive AI Chipset Market size was valued at over USD 2.3 billion in 2022 and is anticipated to grow at a CAGR of over 20% between 2023 & 2032. The pursuit of autonomous driving technology is a primary factor driving market growth. The rising preference for Electric Vehicles (EVs) is driving the need for efficient power management, and AI chipsets play a crucial role in optimizing battery performance. Automotive manufacturers are also increasingly integrating AI chipsets to enable vehicles to perceive and respond to their surroundings autonomously, enhancing safety & convenience. In-car infotainment systems and Advanced Driver Assistance Systems (ADAS), which are high in demand, also rely heavily on AI processing power. The quest for eco-friendly and connected vehicles will propel the adoption of AI chipsets over the forecast period.
Automotive AI Chipset Market Key Takeaways
Automotive AI chipsets are specialized integrated circuits designed to perform advanced data processing tasks within vehicles. These chipsets incorporate artificial intelligence and machine learning capabilities to analyze data from various vehicle sensors, cameras & radars.
As AI chipsets gather and process vast amounts of data from vehicles, concerns over data privacy & security arise. Mishandling or breach of this data can lead to privacy infringements and raise legal & ethical questions for automotive companies. Also, the research, development, and manufacturing of cutting-edge AI chipsets require substantial investments. This can strain the budgets of automotive manufacturers, particularly smaller companies, potentially limiting their ability to adopt these technologies.
COVID-19 Impact
The COVID-19 pandemic had multifaceted impacts on the automotive AI chipset market. Initially, the global automotive industry experienced a slowdown in production & sales due to lockdowns and economic uncertainties. However, as the world adapted to the pandemic, the significance of AI-driven technologies became even more pronounced. Consumers sought safer and more connected vehicles, accelerating the adoption of AI-driven features such as touchless interfaces, autonomous capabilities, and advanced navigation systems. Moreover, the pandemic highlighted the importance of AI in optimizing supply chains, fleet management, and remote vehicle diagnostics, further driving the demand for AI chipsets in the automotive sector.
Automotive AI Chipset Market Trends
The trend toward edge AI processing involves performing AI computations directly on devices rather than relying on cloud servers. In the automotive context, this ensures real-time decision-making for autonomous vehicles and reduces latency in AI-driven applications. Further, predictive maintenance powered by AI is gaining traction. Automotive AI chipsets are used to monitor vehicle components, predict potential failures, and schedule maintenance, enhancing vehicle reliability & reducing downtime. With the growing complexity of automotive electronics, AI chipsets are utilized for advanced cybersecurity. They help detect and prevent cyber threats, safeguarding connected vehicles from hacking & data breaches. Additionally, collaboration between automakers, AI chipset manufacturers, and tech companies is a prominent trend. These partnerships aim to co-develop AI solutions tailored to the unique requirements of the automotive industry, fostering innovation and market growth.
Automotive AI Chipset Market Analysis
Based on product, the automotive AI chipset market is segmented into GPU, ASIC, FPGA, and CPU. FPGA is expected to witness a significant growth rate of over 24% during the forecast period. These chips can be reprogrammed to accommodate evolving AI algorithms and requirements without requiring physical component changes. In the automotive context, FPGAs are employed for tasks such as adaptive cruise control, lane-keeping assistance, and autonomous driving. Their ability to adapt to changing demands in AI algorithms and the automotive industry's dynamic nature makes FPGAs an essential growth factor in the AI chipset market. They empower manufacturers to keep up with technological advancements and meet evolving safety standards.
Based on vehicle type, the automotive AI chipset market is divided into passenger vehicle and commercial vehicle. The passenger vehicle segment held a dominant market share of over 70% in 2022 and is expected to grow at a lucrative pace till 2032. The adoption of AI chipsets in passenger vehicles is being driven by the demand for enhanced safety, convenience, and driving experiences. In the passenger vehicle segment, AI chipsets are used to power advanced driver-assistance systems that provide features such as adaptive cruise control, lane-keeping assistance, and autonomous emergency braking. These technologies aim to reduce accidents and improve road safety. Additionally, in-car AI-powered infotainment systems offer passengers personalized entertainment and connectivity, enhancing overall driving satisfaction. As consumers increasingly prioritize safety & convenience, the adoption of AI chipsets in passenger vehicles continues to grow.
North America held a significant automotive AI chipset market share of over 30% in 2022. The presence of major automotive manufacturers, tech giants, and a robust research ecosystem has accelerated AI integration into vehicles. The region is at the forefront of autonomous driving technology development with several companies testing autonomous vehicles on North American roads. Moreover, stringent safety regulations and consumer demand for advanced safety features further drive AI chipset adoption in vehicles.
Automotive AI Chipset Market Share
Major companies operating in the automotive AI chipset market are
The competitive landscape is characterized by continuous innovation as companies strive to improve their offerings with new features, integrations, and partnerships.
Automotive AI Chipset Industry News
The Automotive AI chipset market research report includes in-depth coverage of the industry with estimates & forecast in terms of revenue (USD Million) from 2018 to 2032, for the following segments:
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By Product
By Application
By Processing Type
By Vehicle Type
The above information is provided for the following regions and countries:
Table of Contents
Chapter 1 Methodology & Scope
Chapter 2 Executive Summary
Chapter 3 Automotive AI Chipset Market Industry Insights
Chapter 4 Competitive Landscape, 2022
Chapter 5 Automotive AI Chipset Market Estimates & Forecast, By Product (USD Million)
Chapter 6 Automotive AI Chipset Market Estimates & Forecast, By Application (USD Million)
Chapter 7 Automotive AI Chipset Market Estimates & Forecast, By Processing type (USD Million)
Chapter 8 Automotive AI chipset Market Estimates & Forecast, By Vehicle Type (USD Million)
Chapter 9 Automotive AI chipset Market Estimates & Forecast, By Region (Revenue)
Chapter 10 Company Profiles
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The companies listed in this report are a curated selection - not the full competitive universe.
Our market revenue calculations use a bottom-up methodology that accounts for all players across all regions - including manufacturers, distributors, and specialists not individually profiled. The profiles section spotlights strategically significant players; it does not define the scope of our market sizing.
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Research methodology, data sources & validation process
This report draws on a structured research process built around direct industry conversations, proprietary modelling, and rigorous cross-validation and not just desk research.
Our 6-step research process
1. Research design & analyst oversight
At GMI, our research methodology is built on a foundation of human expertise, rigorous validation, and complete transparency. Every insight, trend analysis, and forecast in our reports is developed by experienced analysts who understand the nuances of your market.
Our approach integrates extensive primary research through direct engagement with industry participants and experts, complemented by comprehensive secondary research from verified global sources. We apply quantified impact analysis to deliver dependable forecasts, while maintaining complete traceability from original data sources to final insights.
2. Primary research
Primary research forms the backbone of our methodology, contributing nearly 80% to overall insights. It involves direct engagement with industry participants to ensure accuracy and depth in analysis. Our structured interview program covers regional and global markets, with inputs from C-suite executives, directors, and subject matter experts. These interactions provide strategic, operational, and technical perspectives, enabling well-rounded insights and reliable market forecasts.
3. Data mining & market analysis
Data mining is a key part of our research process, contributing nearly 20% to the overall methodology. It involves analysing market structure, identifying industry trends, and assessing macroeconomic factors through revenue share analysis of major players. Relevant data is collected from both paid and unpaid sources to build a reliable database. This information is then integrated to support primary research and market sizing, with validation from key stakeholders such as distributors, manufacturers, and associations.
4. Market sizing
Our market sizing is built on a bottom-up approach, starting with company revenue data gathered directly through primary interviews, alongside production volume figures from manufacturers and installation or deployment statistics. These inputs are then pieced together across regional markets to arrive at a global estimate that stays grounded in actual industry activity.
5. Forecast model & key assumptions
Every forecast includes explicit documentation of:
✓ Key growth drivers and their assumed impact
✓ Restraining factors and mitigation scenarios
✓ Regulatory assumptions and policy change risk
✓ Technology adoption curve parameter
✓ Macroeconomic assumptions (GDP growth, inflation, currency)
✓ Competitive dynamics and market entry/exit expectations
6. Validation & quality assurance
The final stages involve human validation, where domain experts manually review filtered data to identify nuances and contextual errors that automated systems might miss. This expert review adds a critical layer of quality assurance, ensuring data aligns with research objectives and domain-specific standards.
Our triple-layer validation process ensures maximum data reliability:
✓ Statistical Validation
✓ Expert Validation
✓ Market Reality Check
Trust & credibility
Verified data sources
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Industry journals, trade publications, and specialized media.
Industry databases
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Regulatory filings
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Company reports
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Expert interviews
C-suite, procurement leads, and technical specialists
GMI archive
13,000+ published studies across 20+ industry verticals
Trade data
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Parameters studied & evaluated
Every data point in this report is validated through primary interviews, true bottom-up modelling, and rigorous cross-checks. Read about our research process →