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汽车电子人工智能

市场调查报告书

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1678800

全球汽车市场中的人工智慧 - 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个工作天内

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简介目录

2024 年全球汽车人工智慧市场规模达到 5.145 亿美元，预计到 2032 年将达到 26.09 亿美元，2025-2032 年预测期内的复合年增长率为 22.50%。受人工智慧设计、自动驾驶和个人化客户体验进步的推动，汽车领域的全球生成式人工智慧 (Gen AI) 市场正在快速扩张。对智慧自动化、数据驱动洞察和即时决策的需求推动了 Gen AI 在汽车应用中的采用。

汽车製造商正在整合人工智慧驱动的设计最佳化、预测性维护和智慧车辆系统，以提高安全性和效率。政府倡议、永续发展目标以及连网汽车生态系统的发展进一步支持了市场成长。到 2030 年，人工智慧和电动车对电力的需求预计将比 2023 年增长 55%，而供应量可能仅增长 15% 左右。

生成式人工智慧在自动驾驶汽车开发中的崛起正在加速，特斯拉等公司利用人工智慧进行车队学习和预测分析。特斯拉的自动驾驶系统采用深度学习模型，分析了超过 30 亿英里的驾驶资料，并持续增强 ADAS 功能。

在快速数位化、政府激励措施和基于人工智慧的汽车技术投资的推动下，亚太地区正在经历人工智慧汽车市场最快的成长。中国和日本引领人工智慧汽车融合，并透过政府支持的措施推动智慧移动解决方案。根据中国经济网报道，比亚迪、吉利、东风、奇瑞等主要汽车製造商正在利用人工智慧来提高效率和提供个人化服务。预计2025年中国75%以上的新车将搭载智慧座舱。

动力学

人工智慧自动驾驶汽车需求不断成长

自动驾驶汽车的日益普及极大地推动了汽车领域生成式人工智慧市场的成长。人工智慧高级驾驶辅助系统 (ADAS) 和自动驾驶技术正在透过提高道路安全性和减少人为错误来彻底改变交通方式。这些技术利用人工智慧来分析和应对复杂的驾驶场景，从而提高车辆的安全性和效率。

特斯拉、Waymo、通用汽车等主要汽车製造商正在大力投资人工智慧驱动的自动驾驶系统，这正在加速市场成长。自动驾驶汽车可以避免高达 90% 的人为失误造成的道路交通事故，每年可节省约 1,900 亿美元。事故的大幅减少凸显了人工智慧对道路安全的变革性影响，并强调了自动驾驶汽车在重塑汽车产业的未来潜力。

人工智慧预测性维护和智慧製造

人工智慧预测性维护和智慧製造正在透过提高营运效率和成本效益来改变汽车产业。预测分析在优化生产线、检测缺陷和减少停机时间方面发挥着至关重要的作用。这种方法使製造商能够在潜在问题恶化之前预测并解决它们，从而显着提高整体生产力和可靠性。例如，预测性维护可以减少意外故障，这对于维持持续生产和降低维修成本特别有利。

人工智慧与预测性维护的结合可减少 70% 的意外故障、提高 25% 的营运生产力并降低 25% 的维护成本。此外，人工智慧驱动的品质控制系统可确保高生产标准和最少错误。例如，宝马雷根斯堡工厂在汽车组装过程中采用了先进的分析系统，可以提前发现潜在故障，大大减少汽车组装过程中的中断。这种积极主动的方法不仅提高了安全性和效率，而且有助于实现更永续和更可靠的製造过程。

实施成本高且资料隐私问题

生成式人工智慧与汽车领域的整合将带来重大变革，但也面临巨大的挑战。最大的障碍之一是实施成本高。人工智慧驱动的解决方案需要在硬体、软体和培训方面进行大量投资。例如，在汽车製造中实施人工智慧每个工厂的成本高达 5 亿美元。这种财务负担对许多公司来说是巨大的，成为广泛采用的重大障碍。

另一个关键挑战是资料隐私问题，尤其是人工智慧驾驶员监控系统。这些系统引发了必须解决的监管问题，以确保合规性并维护消费者信任。资料隐私和安全至关重要，因为它们直接影响围绕人工智慧技术的监管环境。解决这些问题对于持续的市场成长和生成式人工智慧在汽车产业的成功整合至关重要。透过克服这些挑战，公司可以充分发挥人工智慧的潜力，增强设计、製造和客户体验，最终推动该领域的创新和竞争力。

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

15. Appendix

15.1. About Us and Services
15.2. Contact Us

02-2729-4219

+886-2-2729-4219

License/价格

全球汽车市场中的人工智慧 - 2025 年至 2032 年

Global Gen AI in Automotive Market - 2025-2032

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

第五章：产业分析

第 6 章：按组件

第 7 章：按系统类型

第 8 章：按技术

第 9 章：按流程

第 10 章：按应用

第 11 章：可持续性分析

第 12 章：按地区

第 13 章：竞争格局

第 14 章：公司简介

第 15 章：附录