자동차용 AI 가속기 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the automotive AI accelerator market size is expected to grow from USD 10.96 billion in 2025 to USD 14.85 billion in 2026 and is forecast to reach USD 63.21 billion by 2031 at 33.60% CAGR over 2026-2031.

This report is Segmented by Offering (Hardware, and Software), Processor Type (GPU-Based Accelerators, NPU / AI ASIC Accelerators, and More), Application (ADAS and Active Safety, Autonomous Driving and Robotaxi Compute, Intelligent Cockpit and In-Cabin AI, and More), Vehicle Type (Passenger Vehicles, and Commercial Vehicles), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Automotive AI Accelerator Market Trends and Insights

Rising Adoption Of ADAS And Active Safety Functions

Euro NCAP's 2026 revision changed how new vehicles are assessed and introduced a 100-point Safe Driving category, which raised the practical compute threshold for mainstream safety programs in Europe. This has pushed the automotive AI accelerator market toward larger ADAS procurement programs because features such as braking support, lane assistance, and driver monitoring now carry greater approval weight in volume vehicles. Mobileye said in January 2026 that its 8-year revenue pipeline reached USD 24.5 billion at the end of 2025, showing how design wins have expanded across large OEM programs. It also announced future delivery of more than 19 million EyeQ6H-based Surround ADAS systems across 2 top-10 automaker programs, which shows that AI-enabled safety hardware is moving into very large production runs. As these functions shift from premium trims into broader lineups, the automotive AI accelerator market gains volume, while suppliers face pressure to bundle more functions on each compute platform to protect margins.

Growing Demand For On-Vehicle Real-Time Inference

The automotive AI accelerator market is seeing stronger demand for on-vehicle inference because safety, responsiveness, and feature reliability depend on local processing rather than delayed remote execution. Horizon Robotics said its Journey 6 series was being deployed across more than 100 vehicle models in 2025 and was tracking toward 10 million cumulative units, which signals that production programs are favoring local AI execution at scale. Qualcomm and Leapmotor presented a central computer in January 2026 that combines Snapdragon Cockpit Elite and Ride Elite on one architecture, which reflects a clear move toward real-time multi-domain processing inside the vehicle. This shift matters for the automotive AI accelerator market because local inference does not just add compute demand, it changes the kind of compute demand, favoring platforms that can sustain AI tasks within tight automotive power, packaging, and safety limits. It also raises the value of processors that can handle cockpit, sensing, and active safety workloads together, since OEMs gain lower latency and simpler software coordination from that setup.

High Thermal Design And Power Efficiency Constraints

Thermal design remains a real restraint for the automotive AI accelerator market because higher compute density adds cooling, packaging, and energy management burdens inside the vehicle. The issue is more visible in EV programs, where added compute draw can work directly against range and thermal stability targets, especially when several AI functions run at the same time. STMicroelectronics introduced Stellar P3E in February 2026 as the first automotive microcontroller with an integrated Neural-ART accelerator, and the product was positioned for edge AI use cases that need a lower thermal footprint than larger central processors. That product direction shows why the automotive AI accelerator market is not only rewarding raw TOPS growth, it is also rewarding better efficiency and more task-specific silicon. Vendors that cannot balance performance with automotive power budgets risk losing share in volume programs, even when their compute capability remains technically strong.

Other drivers and restraints analyzed in the detailed report include:

1. Software-Defined Vehicle Architectures Increasing Centralized Compute Demand
2. Expansion Of Level 2 Plus And Level 3 Automation Programs
3. Functional Safety And Validation Complexity

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Hardware accounted for 64.46% of revenue in 2025, which shows that the automotive AI accelerator market still depends first on silicon, memory access, packaging, and board-level integration before software monetization can scale. This mix fits the current stage of the market because OEMs must first secure the compute base required for ADAS, cockpit, and future autonomy programs. The hardware layer also remains the part of the stack with the largest upfront cost, since automotive-grade qualification, long product life requirements, and vehicle integration all push spending toward proven semiconductor platforms. In the automotive AI accelerator market, this keeps hardware spending elevated even when software becomes a larger source of future margin. The segment's position also reflects the fact that compute capability must be installed before update-led business models can generate recurring value.

Software is still the fastest-growing offering segment, with a 33.88% CAGR expected through 2031, and that direction says the value pool is gradually broadening beyond one-time chip sales. The automotive AI accelerator industry is shifting toward systems where features can be activated, refined, or extended through software updates once the underlying compute has already been placed in the vehicle. Qualcomm's mixed-domain central compute approach and STMicroelectronics' edge AI controller launch both point to platforms where multiple software workloads can share the same silicon foundation. That means software growth is not replacing hardware growth inside the automotive AI accelerator market, it is building on top of it by making each installed compute platform economically useful for longer. As vehicle architectures centralize, software also becomes harder to separate from hardware choice, which raises switching costs and makes platform ecosystems more important.

GPU-based accelerators held 37.22% of revenue in 2025, which shows that the automotive AI accelerator market still leans on established high-compute platforms and their mature software environments. GPUs entered the category with a clear advantage in autonomous driving development because they supported large neural workloads, broad tool access, and easier scaling from research into production. That installed base still matters because many OEM and robotaxi programs have already built perception and planning stacks around GPU-compatible environments. In the automotive AI accelerator market, this gives GPU suppliers an advantage in high-end domain controllers and complex autonomy programs where software continuity matters as much as raw performance. It also explains why leadership at the top end can remain durable even as other processor types expand faster.

NPU and AI ASIC platforms are projected to grow at a 34.09% CAGR through 2031, which shows that the next phase of the automotive AI accelerator market is likely to reward inference efficiency more directly. Horizon Robotics said its Journey 6 family was being deployed across more than 100 models, which shows how purpose-built AI compute can move beyond pilot programs into broad production ADAS volumes. STMicroelectronics also positioned its Stellar P3E for edge intelligence tasks that benefit from integrated AI acceleration with a smaller thermal and system burden than larger multi-chip setups. Heterogeneous SoCs will still matter because many programs need CPU, graphics, signal, and AI resources together, but the automotive AI accelerator industry is clearly moving toward more specialized inference blocks within those broader designs. As thermal limits, power budgets, and cost pressure become tighter, the fastest gains are likely to come from processor types that can deliver more useful local AI per watt rather than the highest theoretical performance.

Complete Report Scope:

• By Offering
  • Hardware
  • Software
• By Processor / Accelerator Type
  • GPU-Based Accelerators
  • NPU / AI ASIC Accelerators
  • FPGA-Based Accelerators
  • DSP / Vision Processing Accelerators
  • Heterogeneous AI SoCs
• By Application
  • ADAS and Active Safety
  • Autonomous Driving and Robotaxi Compute
  • Intelligent Cockpit and In-Cabin AI
  • Telematics and Connected Vehicle Services
  • Predictive Maintenance and Fleet Intelligence
• By Vehicle Type
  • Passenger Vehicles
  • Commercial Vehicles
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

Asia-Pacific accounted for 38.18% of revenue in 2025, giving the region the leading position in the automotive AI accelerator market. China remains central to that lead because the region combines strong EV production, active ADAS rollout, and growing interest in closer chip and automaker coordination. Horizon Robotics said its Journey 6 series was being deployed across more than 100 vehicle models, which highlights how local AI compute platforms are gaining broad production relevance in the region. Japan also remains important through established semiconductor and vehicle electronics capability, and Renesas continues to position the R-Car V4H for Level 2 Plus and Level 3 use cases with NCAP-focused functionality. South Korea adds strength through automotive-grade memory supply, while India is becoming a more visible destination for future programs after Mobileye reported a major Mahindra design win covering at least 6 vehicle models.

The Middle East and Africa are expected to grow at a 34.32% CAGR through 2031, making it the fastest-growing regional block in the automotive AI accelerator market. This growth does not rest on broad domestic vehicle manufacturing, but on fast-moving smart mobility deployment, public backing for autonomous transport, and a willingness to commercialize advanced services early. Dubai's Roads and Transport Authority launched commercial autonomous taxi operations through Uber and Apollo Go in March 2026, and WeRide also began fully driverless commercial robotaxi operations in Dubai with Uber. Those moves matter because they create live demand for compute platforms that can support production-grade autonomy in real operating conditions. In the automotive AI accelerator market, this makes the region a smaller revenue base today, but an important signal market for future deployment models.

Europe and North America form the next major regional cluster in the automotive AI accelerator market, though their strengths are different. Europe is being pushed by tighter safety frameworks and faster ADAS content expansion, which supports higher compute needs in mainstream production programs. Qualcomm's jointly developed system with BMW and NVIDIA's work with Mercedes-Benz show how Europe remains important for premium vehicle programs and globally deployable software-hardware stacks. North America remains highly relevant in autonomous trucking and advanced compute deployment, while South America is still an early-stage market where AI content mainly enters through imported vehicles that already meet stricter safety expectations elsewhere.

1. NVIDIA Corporation
2. Qualcomm Technologies, Inc.
3. NXP Semiconductors N.V.
4. Renesas Electronics Corporation
5. Texas Instruments Incorporated
6. Intel Corporation
7. Mobileye Global Inc.
8. Advanced Micro Devices, Inc.
9. STMicroelectronics N.V.
10. Infineon Technologies AG
11. Ambarella, Inc.
12. Arm Holdings plc
13. Micron Technology, Inc.
14. Robert Bosch GmbH
15. Aptiv PLC
16. Continental AG
17. Horizon Robotics
18. Hailo Technologies Ltd.
19. Kneron, Inc.
20. SiMa.AI

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Adoption of ADAS and Active Safety Functions
  • 4.2.2 Growing Demand for On-Vehicle Real-Time Inference
  • 4.2.3 Software-Defined Vehicle Architectures Increasing Centralized Compute Demand
  • 4.2.4 Expansion of Level 2 Plus and Level 3 Automation Programs
  • 4.2.5 Increasing AI Content in Intelligent Cockpits and Driver Monitoring
  • 4.2.6 Automaker and Semiconductor Co-Development of Automotive Edge AI Platforms
• 4.3 Market Restraints
  • 4.3.1 High Thermal Design and Power Efficiency Constraints
  • 4.3.2 Functional Safety and Validation Complexity
  • 4.3.3 Long Qualification Cycles and Automotive Grade Supply Risk
  • 4.3.4 Cybersecurity Exposure in Multi-Domain AI Architectures
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Market Positioning Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 5.1.1 Hardware
  • 5.1.2 Software
• 5.2 By Processor / Accelerator Type
  • 5.2.1 GPU-Based Accelerators
  • 5.2.2 NPU / AI ASIC Accelerators
  • 5.2.3 FPGA-Based Accelerators
  • 5.2.4 DSP / Vision Processing Accelerators
  • 5.2.5 Heterogeneous AI SoCs
• 5.3 By Application
  • 5.3.1 ADAS and Active Safety
  • 5.3.2 Autonomous Driving and Robotaxi Compute
  • 5.3.3 Intelligent Cockpit and In-Cabin AI
  • 5.3.4 Telematics and Connected Vehicle Services
  • 5.3.5 Predictive Maintenance and Fleet Intelligence
• 5.4 By Vehicle Type
  • 5.4.1 Passenger Vehicles
  • 5.4.2 Commercial Vehicles
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Qualcomm Technologies, Inc.
  • 6.4.3 NXP Semiconductors N.V.
  • 6.4.4 Renesas Electronics Corporation
  • 6.4.5 Texas Instruments Incorporated
  • 6.4.6 Intel Corporation
  • 6.4.7 Mobileye Global Inc.
  • 6.4.8 Advanced Micro Devices, Inc.
  • 6.4.9 STMicroelectronics N.V.
  • 6.4.10 Infineon Technologies AG
  • 6.4.11 Ambarella, Inc.
  • 6.4.12 Arm Holdings plc
  • 6.4.13 Micron Technology, Inc.
  • 6.4.14 Robert Bosch GmbH
  • 6.4.15 Aptiv PLC
  • 6.4.16 Continental AG
  • 6.4.17 Horizon Robotics
  • 6.4.18 Hailo Technologies Ltd.
  • 6.4.19 Kneron, Inc.
  • 6.4.20 SiMa.ai

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment