自動車におけるAI市場：機会、用途、競合分析

自動車におけるAIは、生成AIの設計から製造、車両利用、EV、自律走行、使用済み自動車に至るまで、バリューチェーン全体を変革する大きな可能性を秘めています。しかし、参入企業は高い投資コストや競争の激化など、技術的・商業的課題を克服しなければなりません。

自動車業界では、自律走行における車両知覚のためのコンピュータビジョンや機械学習、製造におけるロボット工学、車載アシスタントの音声認識のためのNLPなど、数十年にわたりAIが活用されてきました。しかし、「AIが定義する自動車」の時代は、自動車のバリューチェーン全体に影響を与え始めたばかりです。

最新の自律走行車は、様々なセンサーからの膨大なデータをリアルタイムで処理するディープラーニング・アルゴリズムを活用しています。AIによって、車両は交通状況や障害物を含む環境からのリアルタイムデータを分析できるようになり、これは安全なナビゲーションのために極めて重要です。

データ処理がオフラインから車載に移行する中、EVやADASにおける重要な操作のためのデータ処理とリアルタイムの意思決定を改善するAIの需要が高まっています。

AI新興企業への投資は、2024年4月から6月にかけて240億米ドルに急増し、前四半期から2倍以上に増加しました。AIスタートアップは、イーロン・マスクのxAIが60億米ドルを調達し、OpenAIとの競争からスーパーコンピュータ、健康など、業界を問わず2024年第1四半期から第2四半期にかけて275億米ドルを調達しました。Microsoft、BlackRock、NVIDIAは300億米ドル以上を投資しています。データセンターとエネルギー・プロジェクトの建設に300億米ドル以上を投資しています。

本レポートでは、自動車産業におけるAIの戦略、技術、市場ポテンシャルを分析し、競合他社の知見を提供します。

目次

第1章 エグゼクティブサマリー

第2章 自動車業界におけるAIの機会

第3章 自動車業界向けAI技術の詳細

• 基礎モデル
• AIハードウェア
• AIソフトウェア
• AIインフラストラクチャ
• シリコンの機会
• 計算と推論
• 生成AIと自動車業界における機会
• NLP
• 機械学習、教師あり、教師なし
• コンピュータービジョン
• AIエージェント
• フェデレーテッドラーニング
• エンタープライズAI

第4章 自動車バリューチェーン全体におけるAIの応用

• 生成AIデザイン：機会、技術、課題、参入企業
• 製造の最適化
• 品質を向上させる方法
• 自動運転におけるAI：CV、DL、ML、生成AI、LLM
• 電気自動車におけるAI：バッテリーからパワートレイン、充電まで
• 車内におけるNLPとGenAI：車内アシスタントからAIコンパニオンへ
• モビリティ・アズ・ア・サービス（MaaS）における人工知能
• 予知保全
• アフターセールスとカスタマーサポートにおけるAI
• 持続可能なモビリティのためのAI：リサイクル、再利用、寿命の終了

第5章 主要なパートナーシップとエコシステムの開発

第6章 自動車業界におけるAIへの最大の投資

第7章 規制と倫理基準の進化

• AIを規制する理由
• EU AI法
• 米国のAI政策
• 中国の規制とAI標準
• 倫理的なAIと自動車へのアプローチ

第8章 CXOへのインタビュー

第9章 競合と参入企業の戦略

第10章 イノベーションハブ

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