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汽車電子人工智能

市場調查報告書

商品編碼

1678800

全球汽車市場中的人工智慧 - 2025 年至 2032 年

Global Gen AI in Automotive Market - 2025-2032

出版日期: 2025年03月11日 | 出版商:

DataM Intelligence | 英文 225 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球汽車人工智慧市場規模達到 5.145 億美元，預計到 2032 年將達到 26.09 億美元，2025-2032 年預測期內的複合年成長率為 22.50%。受人工智慧設計、自動駕駛和個人化客戶體驗進步的推動，汽車領域的全球生成式人工智慧 (Gen AI) 市場正在快速擴張。對智慧自動化、數據驅動洞察和即時決策的需求推動了 Gen AI 在汽車應用中的採用。

汽車製造商正在整合人工智慧驅動的設計最佳化、預測性維護和智慧車輛系統，以提高安全性和效率。政府舉措、永續發展目標以及連網汽車生態系統的發展進一步支持了市場成長。到 2030 年，人工智慧和電動車對電力的需求預計將比 2023 年成長 55%，而供應量可能僅成長 15% 左右。

生成式人工智慧在自動駕駛汽車開發中的崛起正在加速，特斯拉等公司利用人工智慧進行車隊學習和預測分析。特斯拉的自動駕駛系統採用深度學習模型，分析了超過 30 億英里的駕駛資料，並持續增強 ADAS 功能。

在快速數位化、政府激勵措施和基於人工智慧的汽車技術投資的推動下，亞太地區正在經歷人工智慧汽車市場最快的成長。中國和日本引領人工智慧汽車融合，並透過政府支持的措施推動智慧移動解決方案。根據中國經濟網報道，比亞迪、吉利、東風、奇瑞等主要汽車製造商正在利用人工智慧來提高效率和提供個人化服務。預計2025年中國75%以上的新車將搭載智慧座艙。

動力學

人工智慧自動駕駛汽車需求不斷成長

自動駕駛汽車的日益普及極大地推動了汽車領域生成式人工智慧市場的成長。人工智慧高級駕駛輔助系統 (ADAS) 和自動駕駛技術正在透過提高道路安全性和減少人為錯誤來徹底改變交通方式。這些技術利用人工智慧來分析和應對複雜的駕駛場景，從而提高車輛的安全性和效率。

特斯拉、Waymo、通用汽車等主要汽車製造商正在大力投資人工智慧驅動的自動駕駛系統，這正在加速市場成長。自動駕駛汽車可以避免高達 90% 的人為失誤造成的道路交通事故，每年可節省約 1,900 億美元。事故的大幅減少凸顯了人工智慧對道路安全的變革性影響，並強調了自動駕駛汽車在重塑汽車產業的未來潛力。

人工智慧預測性維護和智慧製造

人工智慧預測性維護和智慧製造正在透過提高營運效率和成本效益來改變汽車產業。預測分析在最佳化生產線、檢測缺陷和減少停機時間方面發揮著至關重要的作用。這種方法使製造商能夠在潛在問題惡化之前預測並解決它們，從而顯著提高整體生產力和可靠性。例如，預測性維護可以減少意外故障，這對於維持持續生產和降低維修成本特別有利。

人工智慧與預測性維護的結合可減少 70% 的意外故障、提高 25% 的營運生產力並降低 25% 的維護成本。此外，人工智慧驅動的品質控制系統可確保高生產標準和最少錯誤。例如，寶馬雷根斯堡工廠在汽車組裝過程中採用了先進的分析系統，可以提前發現潛在故障，大大減少汽車組裝過程中的中斷。這種積極主動的方法不僅提高了安全性和效率，而且有助於實現更永續和更可靠的製造過程。

實施成本高且資料隱私問題

生成式人工智慧與汽車領域的整合將帶來重大變革，但也面臨巨大的挑戰。最大的障礙之一是實施成本高。人工智慧驅動的解決方案需要在硬體、軟體和培訓方面進行大量投資。例如，在汽車製造中實施人工智慧每個工廠的成本高達 5 億美元。這種財務負擔對許多公司來說是巨大的，成為廣泛採用的重大障礙。

另一個關鍵挑戰是資料隱私問題，尤其是人工智慧駕駛員監控系統。這些系統引發了必須解決的監管問題，以確保合規性並維護消費者信任。資料隱私和安全至關重要，因為它們直接影響圍繞人工智慧技術的監管環境。解決這些問題對於持續的市場成長和生成式人工智慧在汽車產業的成功整合至關重要。透過克服這些挑戰，公司可以充分發揮人工智慧的潛力，增強設計、製造和客戶體驗，最終推動該領域的創新和競爭力。

Global Gen AI in Automotive Market reached US$ 514.50 million in 2024 and is expected to reach US$ 2,609.00 million by 2032, growing with a CAGR of 22.50% during the forecast period 2025-2032. The global generative AI (Gen AI) market in the automotive sector is experiencing rapid expansion, driven by advancements in AI-powered design, autonomous driving, and personalized customer experiences. The demand for intelligent automation, data-driven insights, and real-time decision-making has fueled the adoption of Gen AI across automotive applications.

Automakers are integrating AI-driven design optimization, predictive maintenance, and intelligent vehicle systems to enhance safety and efficiency. Government initiatives, sustainability goals, and the evolution of connected car ecosystems further support market growth. By 2030, demand for power from AI and EVs is expected to increase by 55% relative to 2023, while supply may grow only by about 15%.

The rise of generative AI in autonomous vehicle development has accelerated, with companies like Tesla leveraging AI for fleet learning and predictive analytics. Tesla's Autopilot system, powered by deep learning models, has analyzed over 3 billion miles of driving data, continuously enhancing ADAS capabilities.

The Asia-Pacific region is witnessing the fastest growth in the Gen AI automotive market, driven by rapid digitalization, government incentives, and investments in AI-based vehicle technologies. China and Japan lead AI-powered vehicle integration, with government-backed initiatives promoting intelligent mobility solutions. According to China Economic Net, major automobile manufacturers such as BYD, Geely, Dongfeng, and Chery are leveraging AI to enhance efficiency and personalise services. It is expected that over 75% of new cars in China will be equipped with intelligent cockpits in 2025.

Dynamics

Rising Demand for AI-Powered Autonomous Vehicles

The increasing adoption of autonomous vehicles is significantly driving the growth of the generative AI market in the automotive sector. AI-powered Advanced Driver Assistance Systems (ADAS) and self-driving technologies are revolutionizing mobility by enhancing road safety and reducing human error. These technologies utilize AI to analyze and respond to complex driving scenarios, thereby improving vehicle safety and efficiency.

Major automakers such as Tesla, Waymo, and General Motors are heavily investing in AI-driven self-driving systems, which is accelerating market growth. Autonomous vehicles have the potential to prevent up to 90% of road accidents caused by human error, significantly saving approximately US$ 190 billion per year. This substantial reduction in accidents highlights the transformative impact of AI on road safety and underscores the future potential of autonomous vehicles in reshaping the automotive industry.

AI-Enabled Predictive Maintenance & Smart Manufacturing

AI-powered predictive maintenance and smart manufacturing are transforming the automotive industry by enhancing operational efficiency and cost-effectiveness. Predictive analytics play a crucial role in optimizing production lines, detecting defects, and minimizing downtime. This approach allows manufacturers to anticipate and address potential issues before they escalate, leading to significant improvements in overall productivity and reliability. For instance, predictive maintenance can reduce unexpected breakdowns, which are particularly beneficial for maintaining continuous production and reducing repair costs.

The integration of AI in predictive maintenance decreases unexpected breakdowns by 70%, boosts operational productivity by 25%, and lowers maintenance costs by 25%. Furthermore, AI-driven quality control systems are ensuring high production standards with minimal errors. For example, BMW's Regensburg plant utilized an advanced analytical system in its vehicle assembly process to identify potential faults early, significantly reducing disruptions in vehicle assembly. This proactive approach not only enhances safety and efficiency but also contributes to a more sustainable and reliable manufacturing process.

High Implementation Costs & Data Privacy Concerns

The integration of generative AI in the automotive sector is poised to bring about significant transformations, but it also encounters substantial challenges. One of the major hurdles is the high cost of implementation. AI-driven solutions necessitate considerable investments in hardware, software, and training. For instance, implementing AI in automotive manufacturing costs up to $500 million per facility. This financial burden be daunting for many companies, making it a significant barrier to widespread adoption.

Another critical challenge is data privacy concerns, particularly with AI-powered driver monitoring systems. These systems raise regulatory issues that must be addressed to ensure compliance and maintain consumer trust. Data privacy and security are paramount, as they directly impact the regulatory environment surrounding AI technology. Addressing these concerns is crucial for sustained market growth and the successful integration of generative AI in the automotive industry. By overcoming these challenges, companies can unlock the full potential of AI to enhance design, manufacturing, and customer experiences, ultimately driving innovation and competitiveness in the sector.

Segment Analysis

The global Gen AI in Automotive market is segmented based on component, vehicle type, technology, process, application, and region.

Passenger Vehicles Represent The Largest Segment

Passenger vehicles dominate the global generative AI in automotive market, with significant adoption of AI-powered solutions. Major automakers like Mercedes-Benz, BMW, and Tesla are at the forefront of integrating generative AI into various aspects of vehicle technology. For instance, Mercedes-Benz has introduced a GPT-powered voice assistant in over 900,000 vehicles, enhancing driver interaction by answering complex queries and providing real-time recommendations. Additionally, BMW's Emotional Intelligence system, featured in the 2024 7 Series, evaluates driver emotions to improve safety and comfort. These advancements underscore the growing role of AI in enhancing the driving experience.

The integration of generative AI extends beyond infotainment systems to autonomous functionalities and design optimization. Tesla's Autopilot system, for example, processes data from millions of vehicles to continuously refine its self-driving algorithms. European Road Safety Council assumes that advanced driver assistance systems will be able to reduce the number of road fatalities by up to 30 percent due to their use of AI. Furthermore, companies like Toyota Research Institute are using generative AI to assist vehicle designers by integrating engineering constraints with creative inputs. This trend highlights the potential for AI to transform both the design and operational efficiency of vehicles in the coming years.

Geographical Penetration

Strong R&D Investments and Regulatory Frameworks Supports Gen AI In North America

North America is at the forefront of the generative AI automotive market, driven by robust R&D investments, supportive regulatory frameworks, and technological advancements. The U.S. and Canada are leading the charge in AI adoption across various sectors, including autonomous driving, predictive analytics, and smart manufacturing. Canada's national AI strategy has invested over $2 billion to support AI and digital research and innovation on sustainable automotive solutions. Similarly, US is actively developing AI-based vehicle safety regulations, further bolstering the region's leadership in this field.

Major automotive companies such as Tesla, Ford, and General Motors are pioneering AI-driven innovations in vehicle connectivity and advanced driver-assistance systems (ADAS). Ford, for instance, uses AI-powered predictive analytics to enhance supply chain resilience, mitigating risks from global semiconductor disruptions. In manufacturing, companies like BMW are leveraging AI for quality control, ensuring superior production standards. Beyond manufacturing, AI is also transforming retail strategies. For instance, CarMax's AI PriceOptimize which is an AI-driven pricing optimization systems that adjust vehicle prices in real-time based on numerous variables, enhancing market competitiveness.

Competitive Landscape

The major global players in the market include Microsoft Corporation, Intel Corporation, Alphabet Inc., Nvidia Corporation, International Business Machines Corporation, Qualcomm Inc., Tesla, Inc, Amazon Web Services, Inc., Accenture, and Advanced Micro Devices, Inc.

Sustainable Analysis

The integration of generative AI in the automotive sector is closely aligned with goals of sustainability, safety, and efficiency. Automakers are utilizing AI to drive design innovation, enhance predictive maintenance, and optimize manufacturing processes. Additionally, AI-powered digital assistants are improving user experiences by offering personalized services, while autonomous driving systems are contributing to enhanced road safety. These advancements are transforming the industry by accelerating innovation cycles, reducing costs, and improving overall vehicle performance.

Despite the promising applications of AI in the automotive industry, challenges such as high implementation costs and ethical concerns regarding AI decision-making remain significant hurdles. However, these challenges have not deterred market players from investing heavily in AI research and development. The industry is poised for sustained growth as companies continue to explore new applications of AI. The European Commission's AI Act is expected to provide much-needed regulatory clarity, which will foster responsible AI deployment in automotive applications. This regulatory framework will likely play a crucial role in ensuring that AI technologies are developed and used ethically and effectively across the sector.

Recent Developments

January 2025, On January 7, Intel announced the availability of the Adaptive Control Unit (ACU), specifically designed for electric vehicle (EV) powertrains and zonal controller applications. The ACU U310 is a cutting-edge processing unit that consolidates multiple real-time, safety-critical, and cybersecure functions into a single chip, enhancing efficiency and security in modern EV architectures.
In December 2024, Waymo, Alphabet's self-driving technology subsidiary, expanded its fully autonomous ride-hailing services in San Francisco and Phoenix. With millions of driverless miles logged, Waymo continues to demonstrate the viability of AI-powered transportation, reshaping urban mobility with its advanced sensor arrays and AI algorithms.
October, 2024, Qualcomm and Alphabet announced a strategic partnership to advance AI-driven automotive solutions, enhancing autonomous capabilities and in-car intelligence. Additionally, Mercedes-Benz inked a significant deal to integrate advanced semiconductor technology into its vehicles, reinforcing the industry's shift toward high-performance computing in mobility.
December 2023, Audi and Reply partnered with Amazon Web Services (AWS) to improve enterprise search experiences using a Generative AI chatbot. The solution, built on Retrieval Augmented Generation (RAG), utilizes AWS tools such as Amazon SageMaker and Amazon OpenSearch Service to enhance data retrieval and operational efficiency.

By Component

Microprocessors
Graphics Processing Unit (GPU)
Field Programmable Gate Array (FPGA)
Memory And Storage Systems
Image Sensors
Biometric Scanners
Others

By Vehicle Type

Passenger Vehicles
Commercial Vehicles

By Technology

Deep Learning
Machine Learning
Computer Vision
Context-Aware Computing
Others

By Process

Signal Recognition
Image Recognition
Data Mining
Others

By Application

Vehicle Design & Manufacturing Optimization
Advanced Driver Assistance Systems (Adas)
Human - Machine Interface (Hmis)
Connected Car Technologies
Autonomous Driving Technologies
Other Applications

By Region

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Why Purchase the Report?

To visualize the global gen AI in automotive market segmentation based on component type, system type, technology, application, end-user, & region.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points at the gen AI in the automotive market level for all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global gen AI in the automotive market report would provide approximately 78 tables, 80 figures, and 225 pages.

Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Vehicle Type
3.3. Snippet by Technology
3.4. Snippet by Process
3.5. Snippet by Application
3.6. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Rising Demand for AI-Powered Autonomous Vehicles
  - 4.1.1.2. AI-Enabled Predictive Maintenance & Smart Manufacturing
- 4.1.2. Restraints
  - 4.1.2.1. High Implementation Costs & Data Privacy Concerns
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Sustainable Analysis
5.6. DMI Opinion

6. By Component

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 6.1.2. Market Attractiveness Index, By Component
6.2. Microprocessors*
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Graphics Processing Unit (GPU)
6.4. Field Programmable Gate Array (FPGA)
6.5. Memory And Storage Systems
6.6. Image Sensors
6.7. Biometric Scanners
6.8. Others

7. By System Type

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By System Type
- 7.1.2. Market Attractiveness Index, By System Type
7.2. Passenger Vehicles*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Commercial Vehicles

8. By Technology

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 8.1.2. Market Attractiveness Index, By Technology
8.2. Deep Learning*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Machine Learning
8.4. Computer Vision
8.5. Context-Aware Computing
8.6. Others

9. By Process

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 9.1.2. Market Attractiveness Index, By Process
9.2. Signal Recognition*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Image Recognition
9.4. Data Mining
9.5. Others

10. By Application

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.1.2. Market Attractiveness Index, By Application
10.2. Vehicle Design & Manufacturing Optimization*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Advanced Driver Assistance Systems (Adas)
10.4. Human - Machine Interface (HMIS)
10.5. Connected Car Technologies
10.6. Autonomous Driving Technologies
10.7. Other Applications

11. Sustainability Analysis

11.1. Environmental Analysis
11.2. Economic Analysis
11.3. Governance Analysis

12. By Region

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 12.1.2. Market Attractiveness Index, By Region
12.2. North America
- 12.2.1. Introduction
- 12.2.2. Key Region-Specific Dynamics
- 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
- 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.2.8.1. US
  - 12.2.8.2. Canada
  - 12.2.8.3. Mexico
12.3. Europe
- 12.3.1. Introduction
- 12.3.2. Key Region-Specific Dynamics
- 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
- 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.3.8.1. Germany
  - 12.3.8.2. UK
  - 12.3.8.3. France
  - 12.3.8.4. Italy
  - 12.3.8.5. Spain
  - 12.3.8.6. Rest of Europe
12.4. South America
- 12.4.1. Introduction
- 12.4.2. Key Region-Specific Dynamics
- 12.4.3. Key Region-Specific Dynamics
- 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
- 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.4.9.1. Brazil
  - 12.4.9.2. Argentina
  - 12.4.9.3. Rest of South America
12.5. Asia-Pacific
- 12.5.1. Introduction
- 12.5.2. Key Region-Specific Dynamics
- 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
- 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.5.8.1. China
  - 12.5.8.2. India
  - 12.5.8.3. Japan
  - 12.5.8.4. Australia
  - 12.5.8.5. Rest of Asia-Pacific
12.6. Middle East and Africa
- 12.6.1. Introduction
- 12.6.2. Key Region-Specific Dynamics
- 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
- 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
- 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

13. Competitive Landscape

13.1. Competitive Scenario
13.2. Market Positioning/Share Analysis
13.3. Mergers and Acquisitions Analysis

14. Company Profiles

14.1. Microsoft Corporation*
- 14.1.1. Company Overview
- 14.1.2. Product Portfolio and Description
- 14.1.3. Financial Overview
- 14.1.4. Key Developments
14.2. Intel Corporation
14.3. Alphabet Inc.
14.4. Nvidia Corporation
14.5. International Business Machines Corporation
14.6. Qualcomm Inc.
14.7. Tesla, Inc
14.8. Amazon Web Services, Inc.
14.9. Accenture
14.10. Advanced Micro Devices, Inc.

LIST NOT EXHAUSTIVE