汽车的AI市场:机会，用途，竞争分析

汽车中的人工智慧具有改变整个价值链的巨大潜力，从生成式人工智慧设计到製造、车辆使用、电动车、自动驾驶和报废车辆。然而，进入者必须克服技术和商业挑战，包括高投资成本和日益激烈的竞争。

汽车产业几十年来一直在使用人工智慧，包括用于自动驾驶中的车辆感知的电脑视觉和机器学习、製造中的机器人技术以及用于车载助理中的语音识别的 NLP。然而， "人工智慧定义汽车" 的时代才刚开始，影响整个汽车价值链。

现代自动驾驶汽车利用深度学习演算法即时处理来自各种感测器的大量数据。人工智慧使车辆能够分析环境中的即时数据，包括交通状况和障碍物，这对于安全导航至关重要。

随着资料处理从离线转移到车载，对人工智慧改善电动车和 ADAS 关键操作的资料处理和即时决策的需求不断增长。

从 2024 年 4 月到 6 月，人工智慧新创公司的投资激增至 240 亿美元，比上一季增加了一倍多。从2024 年第一季到第二季度，各行业的人工智慧新创公司筹集了275 亿美元，从与OpenAI 的竞争到超级电脑再到健康，其中 Elon Musk 的xAI 筹集了60 亿美元。微软、贝莱德和 NVIDIA 已投资超过 300 亿美元。已投资超过300亿美元用于数据中心和能源项目建设。

本报告提供汽车产业上AI的策略，技术，市场潜在力分析，其他竞争公司的知识和见识。

目录

第1章 摘要整理

第2章 汽车产业的AI的机会

第3章 汽车产业的AI技术详细内容

• 基础模式
• AI硬体设备
• AI软体
• AI基础建设
• 硅的机会
• 计算和推论
• 生成AI与汽车产业的机会
• NLP
• 机器学习，有教师，没有教师
• 电脑视觉
• AI代理商
• 联邦学习
• 企业AI

第4章 汽车价值链整体的AI的应用

• 生成式 AI 设计：机会、科技、挑战与进入者
• 製造最佳化
• 如何提高品质
• 自动驾驶中的人工智慧：电脑视觉、深度学习、机器学习、新一代人工智慧、法学硕士
• 电动车中的人工智慧：从电池到动力总成再到充电
• 汽车中的 NLP 和 GenAI：从车上小帮手到 AI 伴侣
• 行动即服务 (MaaS) 中的人工智慧
• 预测性维护
• 人工智慧在售后和客户支援中的应用
• 人工智慧促进永续交通：回收、再利用和报废

第5章 主要伙伴关係和生态系统的开发

第6章 对汽车产业的AI的最大的投资

第7章 法规和伦理标准的演进

• 监管人工智慧的原因
• 欧盟人工智慧法
• 美国人工智慧政策
• 中国法规与人工智慧标准
• 符合道德的人工智慧和汽车方法

第8章 CXO的采访

第9章 竞争和参与企业的策略

第10章 革新集线器

AI in Automotive has huge potential to revolutionise the whole value chain, from generative AI design to manufacturing, vehicle utilization, EVs, Autonomous Driving and end-of-life. But players must overcome techno-commercial challenges including high investment costs and increasing competition. This report analyses the strategies, technologies and market potential of AI in Automotive and provides competitor insights...

The automotive industry has used AI for decades, such as Computer Vision and Machine Learning for vehicle perception in autonomous driving, robotics in manufacturing, and NLP for voice recognition in-car assistants.

However, the era of the "AI-defined Car" is just starting to impact the whole Automotive value chain.

The convergence of innovation breakthroughs in AI, such as huge strides in GAN and advancements in computational power, with commercial readiness and strong investments, unlock opportunities for new revenues, product differentiation, operational efficiency and regulatory compliance.

Auto2x has developed a proprietary methodology of scouting for growth opportunities and prioritising them to help stakeholders turn data into action. Access the Live Ranking with 100+ opportunities of Ai in Automotive.

The Rise of the AI-Defined Vehicle

AI-defined vehicles represent a transformative shift in automotive technology from supervised machine learning to self-learning AI taking central role

An AI-defined vehicle is distinguished by its reliance on artificial intelligence (AI) as a central component in driving operations, vehicle management, and user interactions.

Unlike traditional vehicles or even many autonomous systems that depend heavily on predefined programming and extensive sensor arrays, AI-defined vehicles use AI algorithms to process and adapt to real-world environments dynamically.

Tesla is a pioneer in this space pushing the boundaries and emphasizing adaptability, scalability, and efficiency over traditional sensor-based autonomy systems.

The Explosion of Vehicle Data and the Shift to On-Board Real-time Processing with AI

Modern autonomous vehicles leverage deep learning algorithms that process extensive data from various sensors in real-time. AI enables vehicles to analyze real-time data from their environment, including traffic conditions and obstacles, which is crucial for safe navigation.

With data processing moving from offline to on-board vehicles, demand for AI to improve data processing and real-time decision-making for critical operations in EVs and ADAS is increasing.

Innovation is marching strong Creating Opportunities for Differentiation and Revenues

AI research output has increased from less than 1 million papers in 2021 to 13 million papers in 2021, an increase of 1300%.

The analysis of the Patent Landscape Report on GenAI by the WIPO revealed that Asia companies hold the lion's share in publications. Tencent, Ping An Insurance Group and Baidu own the most GenAI patents.

Strong innovation is helping Asian players develop a competitive advantage and monetize their edge through licensing Standard Essential Patents (SEPs).

Rising Demand for AI Chips for Inference and Model Training Reveal Early Winners

Demand for more and faster Graphic Processing Units (GPUs) is getting stronger as demonstrated by the financial performance of NVIDIA, AMD and other AI leaders. GPUs are used for answering questions on existing models (inference) and during the development phase of an AI model (training).

New Generative AI Applications in Cars Enhance Customer Experiences

Generative AI can enhance automotive design, manufacturing, customer experiences with better communication between drivers and car assistants, as well as vehicle perception for Autonomous Driving from Baidu & Haomo.ai.

Investment in AI in Automotive is Booming To Control AI Infrastructure

• Investments in AI startups surged to $24 billion from April to June 2024, more than doubling from the previous quarter. AI start-ups raised $27.5 Billion in Q1-Q2 2024 across industries, from Elon Musk's xAI raising $6 Billion to race OpenAI to super-computers, health and others.
• Microsoft, BlackRock and NVIDIA are investing more than $30 Billion to build data centres and energy projects to fill the growing demand for AI
• Demand for power to train AI models and process data will skyrocket!
• Amazon spent $2.75 billion on AI startup Anthropic in its largest venture investment yet

Partnerships between AI Leaders and Automotive Players are on the Rise To Enhance Market Positioning

BYD's partnership with NVIDIA focuses on AI training and in-car computing for EVs, highlighting China's strategic push in AI integration.

Players Must Overcome Roadblocks to Unlock the Full Potential of the AI-Defined Car

To realise the full potential of AI in Automotive, players must solve technological challenges in the integration of tech, balance the high investment cost with prioritisation of applications with high ROI and develop in-house expertise to stay relevant.

Furthermore, they will have to protect their Intellectual Property, guarantee safety, privacy and security for their customers and manage regulatory mandates and ethical development requirements.

10 Reasons You Should Read This Report: Our Unique Value Proposition

This report analyses the strategies, technologies and market potential of AI in Automotive to provide competitor insights and actionable guidance.

• 1. Live Ranking of Top 100 Opportunities in Artificial Intelligence to generate revenues, advance products or improve efficiency
• 2. Innovation Roadmaps in +30 key AI technologies incl. CV, ML, GenAI, NLP, EmotionAI, OpticalAI;
• 3. Live Database with +100 AI-Automotive Use Cases across the Automotive Value Chain
• 4. Top20 Player Readiness Scores
• 5. Competitor analysis: Identify strengths and portfolio gaps
• 6. Live AI Start-up Database & Scoring: +1000 start-ups ranked
• 7. Top Innovation Hubs
• 8. Interviews with CXOs
• 9. Continuous data updates through our innovative automated intelligence technology.
• 10. Our report is designed for unlimited access, allowing you to share it freely among your team without any user restrictions

Our Unique Methodology

Live Ranking of Top Opportunities in AI in the Automotive Industry

Auto2x synthesizes innovation metrics, data, expert opinion and proprietary methodologies to develop a long list of disruptive opportunities to innovate, generate new revenues, expand to new markets and improve operational efficiency.

We assess each opportunity based on its Market Potential and Technological Readiness Scores, which are weighted scores comprising TAM (Total Addressable Market), TAM Growth, Competition, Value addition, Investment, Technology Readiness Level (TRL), Patent filings and Scientific research, Scalability and others.

Live Database with AI-Automotive Use Cases From Design to End-of-Life

Auto2x has developed a unique database of AI applications across the value chain in Automotive and use cases in Automotive which is accessible as part of this report. The database unveils:

• how is AI revolutionising the Automotive value chain from design to manufacturing, use and recycling;
• the strategies of carmakers, automotive suppliers, start-ups and tech giants
• their partners and ecosystem to bring the technology to market
• the domains (e.g. Electric cars, Autonomous Driving) these applications focus on
• the technological enablers, such as NLP, ML or Generative AI, empowering the solutions

9 Questions This Report Answers About AI in Automotive:

• 1. Which are the Biggest Opportunities in AI for Automotive to generate revenues, innovate or build competitive advantage?
• 2. How can players overcome the technological and commercial challenges of integrating AI in Automotive?
• 3. Which applications of AI in Automotive have the biggest potential, e.g. TAM or Return-on-Investment)?
• 4. Which innovations in AI are fueling growth in Autonomous Driving and Electric Vehicles?
• 5. What is the future of AI in vehicle interiors?
• 6. How will electric vehicles evolve utilising advancements in artificial intelligence?
• 7. Who are the best partners to build AI-enabled products and improve AI readiness?
• 8. What are the biggest opportunities in AI Automotive Start-ups?
• 9. What is the outlook for AI Intellectual Property in Automotive?

Who Should Read This Report:

• Automotive professionals
• Investment teams
• R&D teams
• Innovation
• Consultants
• Regulators
• Industry Associations

Companies Mentioned: +20 OEMs, +30 Suppliers and +1000 Start-ups

• Carmakers: Aston Martin, Audi, BMW, Honda, Hyundai, KIA, Jaguar Land Rover, Lotus, Mercedes-Benz, Mitsubishi, NIO, Nissan, Porsche, Renault, Smart, Skoda, Tesla, Toyota, Volvo, VW, XPeng, Zeekr
• Suppliers: Amazon, Alibaba, AMD, Apple, arm, Autoneum, Axios, Baidu, Blackberry, Continental, Denso, Foxconn, Google, Harman, Helm.ai, Hitachi, Hyundai Mobis, Huawei, IBM, Intel, Kodiak Robotics, Lear, Lenovo, Mando HL, Microsoft, Mobileye, NVIDIA, Qualcomm, Texas Instruments, TSMC, Valeo, ZF
• Start-ups: +1000, including AiAthena, Recogni,
• MaaS: Waymo, Uber, Lyft, TIER, Pony
• Regulators
• Foundational Models, LLMs: Anthropic, Meta, Google, OpenAI, xAI
• Other: Accenture, Bain, CitiGroup, Goldman Sachs

Table of Contents

1. Executive Summary

2. The Opportunity in AI in Automotive

• 1. Ranking of +100 Opportunities in AI for Automotive by Market Potential and Tech Readiness
• 2. Analysis of +100 Opportunities by Market Potential
• 3. Assessment of each Opportunity by Technology Readiness Level
• 4. Recommendations for Incumbents and New Market Entrants

3. Deep Dive in AI Technology for the Automotive Industry

• 1. Foundation Models
• 2. AI Hardware
• 3. AI Software
• 4. AI Infrastructure
• 5. Silicon Opportunities
• 6. Compute and Inference
• 7. Generative AI and opportunities in Automotive
• 8. NLP
• 9. Machine Learning, Supervised, Unsupervised
• 10. Computer Vision
• 11. AI Agents
• 12. Federated Learning
• 13. Enterprise AI

4. Applications of AI across the Automotive Value Chain

• 1. Generative AI Design: Opportunities, Technologies, Challenges and Players
• 2. Manufacturing Optimisation
• 3. How to Improve Quality
• 4. AI in Autonomous Driving: CV, DL, ML, Generative AI and LLMs
• 5. AI in Electric Vehicles: from batteries to powertrains and charging
• 6. NLP and GenAI in Vehicle Interiors: From In-car Assistants to AI Companions
• 7. Artificial Intelligence in Mobility-as-a-Service (MaaS)
• 8. Predictive Maintainance
• 9. AI in Aftersales and Customer Support
• 10. AI for Sustainable Mobility: Recycling, Repurposing, End of life

5. Key Partnerships and the Development of Ecosystems

6. The Biggest Investments in AI in the Automotive Industry

7. Evolving Regulation and Ethical Standards

• 1. Why Regulate AI
• 2. EU AI Act
• 3. US AI Policy
• 4. China's Regulation and AI Standards
• 5. Ethical AI and Automotive Approaches

8. Interviews with CXOs

9. Competition and Player Strategies

• 1. Carmakers: Leaders vs. Laggards
• 2. Suppliers: Readiness Levels
• 3. Tech Giants: New Entrants and Emerging Leaders
• 4. Start-ups: Emerging Stars

10. Innovation Hubs