サンプル依頼リスト カート
  • English
  • Korean
デフォルト表紙
市場調査レポート
商品コード
1704031

中国のOEMのAI定義車両戦略（2025年）

Chinese OEMs' AI-Defined Vehicle Strategy Research Report, 2025

出版日
ページ情報
英文 420 Pages
納期
即日から翌営業日
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
中国のOEMのAI定義車両戦略（2025年）
出版日: 2025年04月07日
発行: ResearchInChina
ページ情報: 英文 420 Pages
納期: 即日から翌営業日
GIIご利用のメリット
  • 全表示
  • 概要
  • 目次
概要

1. AI定義車両は、データ、コンピューティングパワー、モデルという3つの主な要素の深い結合に依存しています。

データとは、車両が走行し、外部環境と相互作用する際に収集されるさまざまなタイプの情報を指します。これはAI定義車両の「燃料」として機能し、アルゴリズムのトレーニングと最適化のための基本的な材料を提供します。コンピューティングパワーには、データを処理しコンピューティングタスクを実行するクラウドコンピューティングセンターと車両AIチップが含まれます。インテリジェントカーの「エンジン」として機能し、システム性能の上限を決定します。モデルとは、AI理論や数学モデルに基づくさまざまな計算ステップやルールを指し、データを処理・分析し、特定のインテリジェント機能を実現するために使用されます。自動車の「頭脳」として機能し、インテリジェンスのレベルを決定します。

OEMは3つの要素を同時に展開する必要があります。データの面では、全シナリオをカバーする能力を確立する必要があり、コンピューティングパワーの面では、チップのエネルギー効率のボトルネックを解消する必要があり、モデルの面では、車両とクラウドの協調推論を実現する必要があります。AI定義車両の究極の姿は、3つの要素の深い結合に依存し、「データは使うほどに洗練され、コンピューティングパワーはより高く、より効率的になり、モデルは訓練によって向上する」という自己進化システムを形成します。

2. インテリジェントドライビングAIの急速な反復において、2025年にVLAモデルをめぐる競争が始まります。

インテリジェントドライビングにおけるAI技術は、従来のCNNからBEV+Transformer（2023年）、エンドツーエンド（2024年）、エンドツーエンド+VLM（2024年後半）、VLA（2025年）へと、異例の速さで進化・反復します。VLAは、「知覚と判断の分離」から「知覚、推論、実行の統合」へのインテリジェントドライビング技術のパラダイム飛躍を示します。

VLA（Vision-Language-Action）モデルは、従来のエンドツーエンドインテリジェントドライビングの進化形として、マルチモーダル融合（視覚+言語+実行）と思考連鎖推論を通じて、現在のインテリジェントドライビングシステムが抱える3つの中核的課題、すなわち世界的な意思決定能力、解釈可能性の飛躍的向上、汎化性能の飛躍的向上を解決します。

Li Auto、Xpeng、Geely、Xiaomiはいずれも、2025年から順次VLAを自動車に搭載する計画を発表しています。その他のOEMも、異なる（あるいは類似の）技術経路を採用しながらも、AIの統合に遅れをとってはいません。

2025年は、VLAベースのインテリジェントドライビングソリューションの「シンギュラリティモーメント」になる可能性があります。VLAの採用は単なる技術的アップグレードではなく、インテリジェントカーを単なる「ツール」から「エージェント」へと変貌させるものです。この競争では、データベース、コンピューティングパワーの優位性、人気の車種を持つ企業が、今後10年の自動車産業で発言力を持つとみられます。消費者にとっては、より人間的なモビリティ体験と市場競合の激化が、2025年の中国インテリジェント自動車産業の2つの背景色となります。

3. 自動車メーカー各社はAIの展開と自動車への応用のペースを速めています。

Li AutoのAI定義車両のレイアウトを見ると、2024年以降、同社は車両インテリジェンスの好況期に入っています。業界初のエンドツーエンド+VLMデュアルシステムインテリジェントドライビングと「駐車スペースから駐車スペースへ」のインテリジェントドライビングを展開し、2025年第3四半期には次世代自動運転アーキテクチャ「Mind VLA」の量産と実装を計画しています。

Li Autoは2021年に車両運行システムの研究開発プロジェクトを開始しました。200人のチームと10億元を超える研究開発費を投入し、ソリューションの選定、アーキテクチャ設計、実装を完了しました。最初のバージョンは2024年に量産され、自動車に採用されました。2025年3月に開催された2025 ZGC Forum Annual Conferenceで、Li AutoのLi Xiang会長は、同社が車両OSをオープンソース化すると発表しました。Li Autoの試算によると、Halo OSのオープンソース化により、冗長な研究開発投資を排除することで、自動車産業は年間100億～200億元のコスト削減が可能となり、中国におけるAI定義車両の開発がさらに加速します。

当レポートでは、中国の自動車産業について調査分析し、AI定義車両の概念、ソフトウェア定義車両との違い、AI定義自動車の3つの主な要素、主要OEMの戦略とレイアウトなどの情報を提供しています。

目次

第1章 AI定義車両の概要

  • AI定義車両 vs. ソフトウェア定義車両（1）
  • AI定義車両 vs. ソフトウェア定義車両（2）
  • AI定義車両の3つの主な要素（1）
  • AI定義車両の3つの主な要素（2）
  • AIが自動車産業のパターンを再形成している
  • AI定義車両がもたらす輸送産業の変化
  • AI定義車両時代のヒューマンマシン協調モデル
  • AI定義車両が都市ガバナンスモデルの変化を促進する
  • AI定義車両が未来の輸送の到来を加速させる
  • AI定義車両とソリューションの課題

第2章 OEMのAIインフラ層のレイアウト：データ+コンピューティングパワー

  • AI定義車両インフラ層：データ
  • データはAI技術の中核となる原材料である
  • AI定義車両インフラ層：クラウドコンピューティングパワー
  • AI定義車両インフラ層：車両コンピューティングパワー

第3章 OEMのAIモデル層レイアウト

  • 自動車部門におけるAI基盤モデルの応用の概要
  • 車両チップにおけるAI基盤モデルの要件
  • 車両オペレーティングシステムにおけるAI基盤モデルの応用
  • インテリジェントドライビングにおけるAI基盤モデルの応用
  • インテリジェントコックピットとインタラクションにおけるAI基盤モデルの応用
  • OEMのAI基盤モデルの応用のサマリー
  • サプライヤーのAI基盤モデルの応用のサマリー
  • 中国の主流AI基盤モデルのサマリー
  • 自動車部門におけるAI基盤モデルの応用における課題と開発動向

第4章 OEMが研究開発、生産、販売、サービスなどの分野でAIを応用する方法

  • AI技術がOEMをチェーン全体にわたり強化する：研究開発、生産、販売、サービス、サプライチェーン管理（1）
  • AI技術がOEMをチェーン全体にわたり強化する：研究開発、生産、販売、サービス、サプライチェーン管理（2）
  • 研究開発・設計におけるAI技術の応用：SoCの研究開発・設計（1）
  • 研究開発・設計におけるAI技術の応用：SoCの研究開発・設計（2）
  • 研究開発・設計におけるAI技術の応用：SoCの研究開発・設計（3）
  • 研究開発・設計におけるAI技術の応用：SoCの研究開発・設計（4）
  • 研究開発と設計におけるAI技術の応用：インテリジェントコックピットインタラクション
  • 研究開発・設計におけるAI技術の応用事例
  • 自動車生産におけるAI技術の応用
  • 自動車生産におけるAI技術の応用事例（1）
  • 自動車生産におけるAI技術の応用事例（2）
  • 自動車生産におけるAI技術の応用：OEMの応用事例のサマリー（1）
  • 自動車生産におけるAI技術の応用：OEMの応用事例のサマリー（2）
  • 販売・サービスにおけるAI技術の応用
  • 販売・サービスにおけるAI技術の応用：OEMの適用事例のサマリー
  • OEMがAIチームを構築する方法（1）
  • OEMがAIチームを構築する方法（2）
  • OEMのAIチーム構築事例（1）
  • OEMのAIチーム構築事例（2）
  • OEMのAIチーム構築事例（3）

第5章 AI定義車両におけるOEMの進捗とレイアウト

  • Li Auto
  • NIO
  • Xpeng
  • Xiaomi Auto
  • Geely
  • BYD
  • Changan
  • BAIC
  • Great Wall Motor
  • Chery
  • SAIC
目次
Product Code: ZXF011

AI-Defined Vehicle Report: How AI Reshapes Vehicle Intelligence?

Chinese OEMs' AI-Defined Vehicle Strategy Research Report, 2025, released by ResearchInChina, studies, analyzes, and summarizes the concept of AI-defined vehicles, the differences between AI-defined vehicles and software-defined vehicles, the three key elements (data, computing power, and model) of AI-defined vehicles, the strategies and layout of mainstream OEMs in these three elements, how AI enables intelligent vehicle manufacturing, and the AI strategies and layout of mainstream OEMs in areas such as intelligent driving and intelligent cockpit.

AI-defined vehicles refer to a new generation of vehicles that use artificial intelligence (AI) technology as the core driving force to reshape the full lifecycle of vehicles, involving R&D, design, production, usage, and services, in an all-round way. The core of AI-defined vehicles lies in feeding data and training rule-free AI foundation models to improve understanding, perception, and data decision capabilities in complex scenarios. The rapid iteration of AI foundation models marks a turning point from software-defined vehicles to AI-defined vehicles, that is, rule-based intelligent algorithms are being replaced by more flexible core AI technologies. From a technical perspective, "software-defined vehicles" emphasize expanding functionality through software upgrades, while the introduction of AI technology enables vehicle intelligence to break through fixed rules, giving vehicles the ability to learn and grow on their own.

AI-defined vehicles: Advance intelligent vehicles from "usable" to "easy to use": Some functions of software-defined vehicles still remain at the "usable" stage, and the shortcomings in accuracy, stability, and intelligent decision-making significantly affects user experience. AI-defined vehicles will reshape intelligent vehicles in multiple aspects, including intelligent cockpit, intelligent driving, and chassis domains, facilitating the evolution of intelligent vehicle products from functionality to capability. This will help to transform vehicles from a mere transportation mean into a "super agent" or a "smart mobility lifeform".

1. AI-defined Vehicles rely on deep coupling of three key elements: data, computing power, and model.

Data refers to various types of information collected when the vehicle travels and interacts with the external environment. It serves as the "fuel" for AI-defined vehicles, providing the basic materials for algorithm training and optimization. Computing power includes cloud computing centers and vehicle AI chips, which process data and execute computing tasks. It acts as the "engine" of intelligent vehicles, determining the upper limit of system performance. Model refers to a range of computing steps and rules based on AI theory and mathematical models, used to process and analyze data and achieve specific intelligent functions. It serves as the "brain" of vehicles, determining the level of intelligence.

OEMs need to simultaneously deploy all the three elements: In terms of data, they need to establish all-scenario coverage capabilities; in terms of computing power, they need to break the energy efficiency bottleneck of chips; and in terms of model, they need to achieve vehicle-cloud cooperative reasoning. The ultimate form of AI-defined vehicles relies on the deep coupling of the three elements, forming a self-evolving system where "data becomes more refined with use, computing power becomes higher and more efficient, and models improve with training".

2. In rapid iteration of intelligent driving AI, competition over VLA models starts in 2025.

AI technology in intelligent driving evolves and iterates at an exceptionally fast pace, from traditional CNNs to BEV+Transformer (2023), end-to-end (2024), end-to-end+VLM (late 2024), and VLA (2025). VLA marks a paradigm leap in intelligent driving technology from "separation of perception and decision" to "integration of perception, reasoning, and execution".

As an advanced form of traditional end-to-end intelligent driving, VLA (Vision-Language-Action) model addresses three core challenges of current intelligent driving systems through multimodal fusion (vision + language + execution) and chain-of-thought reasoning: global decision capability, breakthroughs in interpretability, and a leap in generalization performance.

Li Auto, Xpeng, Geely, and Xiaomi have all announced plans to gradually introduce VLA in their vehicles starting in 2025. Other OEMs, while adopting different (or similar) technology paths, are not lagging in integrating AI.

2025 may become the "singularity moment" for VLA-based intelligent driving solutions. The adoption of VLA is not just a technological upgrade but a transformation of intelligent vehicles from a mere "tool" into an "agent". In this race, companies with data bases, computing power advantages, and popular vehicle models will have a say in the automotive industry in the next decade. For consumers, more humanized mobility experience and fiercer market competition will be dual background colors in China's intelligent vehicle industry in 2025.

3. OEMs are quickening their pace of deploying AI and applying AI in vehicles.

Seen from Li Auto's layout in AI-defined vehicles, since 2024, the company has entered a boom period of vehicle intelligence. It has rolled out industry's first end-to-end + VLM dual-system intelligent driving, and "parking space to parking space" intelligent driving, and plans to mass-produce and implement its next-generation autonomous driving architecture, Mind VLA, in Q3 2025.

Li Auto initiated its vehicle operating system R&D project in 2021. It input a 200-person team and over 1 billion yuan in R&D expense, and has completed solution selection, architecture design and implementation. The first version was mass-produced and used in vehicles in 2024. At the 2025 ZGC Forum Annual Conference in March 2025, Li Xiang, Chairman of Li Auto, announced that the company would open-source its vehicle OS. By Li Auto's estimates, the open-source Halo OS could save the automotive industry 10-20 billion yuan annually by eliminating redundant R&D investments, further accelerating the development of AI-defined vehicles in China.

Since the beginning of 2025, Geely has fully embraced AI, positioning itself as a popularizer of intelligent vehicle AI technology. At CES 2025, Geely unveiled its "Full-Domain AI for Smart Vehicles" technology system. The company believes that true intelligent driving is not just about stacking features but AI enablement.

In the run-up to its product launch in March 2025, Geely partnered with Lifan Technology to establish a joint venture, Chongqing Qianli Intelligent Driving Technology Co., Ltd. Yin Qi, Chairman of Qianli Technology, is also a co-founder of Megvii, one of China's "Four AI Dragons".

According to Yin Qi, AI technology is transitioning from L2 "reasoner" to L3 "agent", and it is the widespread belief in the industry that 2025 is the year of AI application explosion. This trend will first ignite "AI + vehicle".

How will AI define vehicles? Clues may be found in cooperation between Geely and Qianli Technology in three key areas: Ultra-Natural User Interface (NUl), Autonomous Driving & Execution (ADE), and Scaling Law for Al on EV.

Table of Contents

Definitions

1 Overview of AI-Defined Vehicles

  • 1.1 AI-Defined Vehicles vs. Software-Defined Vehicles (1)
  • 1.1 AI-Defined Vehicles vs. Software-Defined Vehicles (2)
  • 1.2 Three Key Elements of AI-Defined Vehicles (1)
  • 1.2 Three Key Elements of AI-Defined Vehicles (2)
  • 1.3 AI Is Reshaping the Automotive Industry Pattern
  • 1.4 Transportation Industry Changes Brought by AI-Defined Vehicles
  • 1.5 Human-Machine Cooperation Models in the Era of AI-Defined Vehicles
  • 1.6 AI-Defined Vehicles Drives Changes in Urban Governance Models
  • 1.7 AI-Defined Vehicles Accelerates the Arrival of Future Transportation Modes
  • 1.8 Challenges in AI-Defined Vehicles and Solutions
    • 1.8.1 Challenges in AI-Defined Vehicles and Solutions (1): Technology
    • 1.8.2 Challenges in AI-Defined Vehicles and Solutions (2): Social Ethics
    • 1.8.3 Challenges in AI-Defined Vehicles and Solutions (3): Industry Standards
    • 1.8.4 Challenges in AI-Defined Vehicles and Solutions (4): Laws and Regulations

2 OEMs' AI Infrastructure Layer Layout: Data + Computing Power

  • 2.1 AI-Defined Vehicle Infrastructure Layer: Data
    • 2.1.1 AI Applications in Vehicle Data Collection, Transmission, and Storage
    • 2.1.2 AI Applications in Vehicle Data Processing, Annotation, and Training
    • 2.1.3 Cases of AI Application in OEMs' Data Closed-Loop (1)
    • 2.1.3 Cases of AI Application in OEMs' Data Closed-Loop (2)
    • 2.1.4 Summary of OEMs' AI Data Closed-Loop Capabilities
    • 2.1.5 Cases of AI Application in Suppliers' Data Closed-Loop Products (1)
    • 2.1.5 Cases of AI Application in Suppliers' Data Closed-Loop Products (2)
    • 2.1.5 Cases of AI Application in Suppliers' Data Closed-Loop Products (3)
    • 2.1.6 Summary of AI Application in Suppliers' Data Closed-Loop Products (1)
    • 2.1.6 Summary of AI Application in Suppliers' Data Closed-Loop Products (2)
    • 2.1.7 Supported by AI Technology, the Ultimate Form of Data Closed-Loop May Be "Self-Evolving System"
    • 2.1.8 Suppliers' AI Data Annotation Application Cases (1)
    • 2.1.8 Suppliers' AI Data Annotation Application Cases (2)
    • 2.1.9 Summary of Suppliers' AI Data Annotation Products (1)
    • 2.1.9 Summary of Suppliers' AI Data Annotation Products (2)
  • 2.2 Data Is the Core Raw Material for AI Technology
    • 2.2.1 Data Has Evolved from an Auxiliary Resource to the Core Material for AI Foundation Models (1)
    • 2.2.1 Data Has Evolved from an Auxiliary Resource to the Core Material for AI Foundation Models (2)
    • 2.2.2 The Scale and Quality of Data Determine Model Performance
  • 2.3 AI-Defined Vehicle Infrastructure Layer: Cloud Computing Power
    • 2.3.1 Requirements for Cloud Computing Power in AI Technology Application and Solutions
    • 2.3.2 How OEMs Build Cloud Computing Power Required by AI (1)
    • 2.3.2 How OEMs Build Cloud Computing Power Required by AI (2)
    • 2.3.3 Cases of OEMs Collaborating with Third Parties to Build Cloud Computing Power Required by AI
    • 2.3.4 Summary of Chinese OEMs' Cloud Computing Power Platforms (Partial)
  • 2.4 AI-Defined Vehicle Infrastructure Layer: Vehicle Computing Power
    • 2.4.1 Requirements for Vehicle Computing Power in AI Technology Applications and Solutions
    • 2.4.2 How OEMs Build Vehicle Computing Power Required by AI
    • 2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (1)
    • 2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (2)
    • 2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (3)
    • 2.4.4 Summary of OEMs' Self-developed Vehicle Computing Chips

3 OEMs' AI Model Layer Layout

  • 3.1 Overview of Application of AI Foundation Models in the Automotive Sector
    • 3.1.1 Definition and Characteristics of AI Foundation Models
    • 3.1.2 Classification of AI Foundation Models and Their Applications in the Automotive Sector
    • 3.1.3 Application of AI Foundation Models in Different Vehicle Layers (1)
    • 3.1.3 Application of AI Foundation Models in Different Vehicle Layers (2)
    • 3.1.4 Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (1)
    • 3.1.4 Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (2)
  • 3.2 Requirements of AI Foundation Models in Vehicle Chips
    • 3.2.1 Deployment of AI Foundation Models on the Terminal Will Continue to Drive Exponential Growth in Vehicle Chip Computing Power Demand
    • 3.2.2 Deployment of AI Foundation Models on the Terminal Calls for High-Compute, Low-Power Compute-in-Memory Chips
    • 3.2.3 Distillation and Compression of AI Foundation Models Can Lower Vehicle Computing Power Requirements
    • 3.2.4 Application Cases of Distillation and Compression of AI Foundation Models
    • 3.2.5 Summary of Vehicle Chips Capable of Running AI Foundation Models
  • 3.3 Applications of AI Foundation Models in Vehicle Operating Systems
    • 3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (1)
    • 3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (2)
    • 3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (3)
    • 3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (4)
    • 3.3.2 Cases of AI Large Model Application in Operating Systems (1)
    • 3.3.2 Cases of AI Large Model Application in Operating Systems (2)
    • 3.3.2 Cases of AI Large Model Application in Operating Systems (3)
    • 3.3.2 Cases of AI Large Model Application in Operating Systems (4)
    • 3.3.3 AI Foundation Models Can Be Used to Generate Autosar Tests
  • 3.4 Application of AI Foundation Models in Intelligent Driving
    • 3.4.1 Application of AI Foundation Models in Intelligent Driving (1)
    • 3.4.1 Application of AI Foundation Models in Intelligent Driving (2)
    • 3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (1)
    • 3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (2)
    • 3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (3)
    • 3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (4)
    • 3.4.3 Application Cases of Generative Simulation Technology for AI Foundation Models (1)
    • 3.4.3 Application Cases of Generative Simulation Technology for AI Foundation Models (2)
    • 3.4.4 Application of AI Foundation Models in Intelligent Driving Perception (1)
    • 3.4.4 Application of AI Foundation Models in Intelligent Driving Perception (2)
    • 3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (1)
    • 3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (2)
    • 3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (3)
    • 3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (4)
    • 3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (1)
    • 3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (2)
    • 3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (3)
    • 3.4.7 Application of AI Foundation Models in Intelligent Driving Decision (1)
    • 3.4.7 Application of AI Foundation Models in Intelligent Driving Decision (2)
    • 3.4.8 Cases of Application of AI Foundation Models in Intelligent Driving Decision by OEMs (1)
    • 3.4.8 Cases of Application of AI Foundation Models in Intelligent Driving Decision by OEMs (2)
    • 3.4.9 Cases of Application of AI Foundation Models in Intelligent Driving Decision by Suppliers (1)
    • 3.4.9 Cases of Application of AI Foundation Models in Intelligent Driving Decision by Suppliers (2)
    • 3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (1)
    • 3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (2)
    • 3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (3)
  • 3.5 Application of AI Foundation Models in Intelligent Cockpit and Interaction
    • 3.5.1 Application of AI Foundation Models in Intelligent Cockpit: AI-Defined Cockpit vs. Software-Defined Cockpit
    • 3.5.2 Application Scenarios of AI Foundation Models in Intelligent Cockpit
    • 3.5.3 Application of AI Foundation Models in Intelligent Cockpit Interaction Design: Enabling Emotional Interaction (1)
    • 3.5.3 Application of AI Foundation Models in Intelligent Cockpit Interaction Design: Enabling Emotional Interaction (2)
    • 3.5.4 Application of AI Foundation Models in Intelligent Cockpit HUD
    • 3.5.5 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (1)
    • 3.5.5 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (2)
    • 3.5.6 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction: Summary of Supplier Solutions
    • 3.5.7 Application of AI Foundation Models in Intelligent Cockpit Gesture Recognition
    • 3.5.8 Application of AI Foundation Models in Intelligent Cockpit Monitoring
    • 3.5.9 AI Algorithms Used by AI Foundation Models in Intelligent Cockpit Monitoring
    • 3.5.10 Cases of Application of AI Foundation Models in Intelligent Cockpit Monitoring (1)
    • 3.5.10 Cases of Application of AI Foundation Models in Intelligent Cockpit Monitoring (2)
    • 3.5.11 Application of AI Foundation Models in Intelligent Cockpit Personalized Services
    • 3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (1)
    • 3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (2)
    • 3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (3)
  • 3.6 Summary of OEMs' AI Foundation Model Applications
  • 3.7 Summary of Suppliers' AI Foundation Model Applications
  • 3.8 Summary of Mainstream AI Foundation Models in China
  • 3.9 Challenges in Application of AI Foundation Models in the Automotive Sector and Development Trends
    • 3.9.1 Challenges in Application of AI Foundation Models in the Automotive Sector and Solutions (1)
    • 3.9.1 Challenges in Application of AI Foundation Models in the Automotive Sector and Solutions (2)
    • 3.9.2 Trend 1 in Application of AI Foundation Models in the Automotive Sector
    • 3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (1)
    • 3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (2)
    • 3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (3)
    • 3.9.4 Trend 3 in Application of AI Foundation Models in the Automotive Sector
    • 3.9.5 Trend 4 in Application of AI Foundation Models in the Automotive Sector

4 How OEMs Apply AI in R&D, Production, Sales, Service, and Other Fields

  • 4.1 AI Technology Empowers OEMs Across the Entire Chain: R&D, Production, Sales, Service, and Supply Chain Management (1)
  • 4.1 AI Technology Empowers OEMs Across the Entire Chain: R&D, Production, Sales, Service, and Supply Chain Management (2)
  • 4.2 Application of AI Technology in R&D and Design: SoC R&D and Design (1)
  • 4.2 Application of AI Technology in R&D and Design: SoC R&D and Design (2)
  • 4.2 Application of AI Technology in R&D and Design: SoC R&D and Design (3)
  • 4.2 Application of AI Technology in R&D and Design: SoC R&D and Design (4)
  • 4.3 Application of AI Technology in R&D and Design: Intelligent Cockpit Interaction
  • 4.4 Cases of Application of AI Technology in R&D and Design
  • 4.5 Application of AI Technology in Vehicle Production
  • 4.6 Cases of Application of AI Technology in Vehicle Production (1)
  • 4.6 Cases of Application of AI Technology in Vehicle Production (2)
  • 4.7 Application of AI Technology in Vehicle Production: Summary of OEMs' Applications (1)
  • 4.7 Application of AI Technology in Vehicle Production: Summary of OEMs' Applications (2)
  • 4.8 Application of AI Technology in Sales and Service
  • 4.9 Application of AI Technology in Sales and Service: Summary of OEMs' Applications
  • 4.10 How OEMs Build AI Teams (1)
  • 4.10 How OEMs Build AI Teams (2)
  • 4.11 Cases of OEMs Building AI Teams (1)
  • 4.11 Cases of OEMs Building AI Teams (2)
  • 4.11 Cases of OEMs Building AI Teams (3)

5 OEMs' Progress and Layout in AI-Defined Vehicles

  • 5.1 Li Auto
    • 5.1.1 AI Layout
    • 5.1.1 Strategy for AI (1)
    • 5.1.1 Strategy for AI (2)
    • 5.1.1 Strategy for AI (3)
    • 5.1.2 AI R&D Investment and Team Building
    • 5.1.3 AI Data Strategy (1)
    • 5.1.3 AI Data Strategy (2)
    • 5.1.3 AI Data Strategy (3)
    • 5.1.3 AI Data Strategy (4)
    • 5.1.4 AI Compute Layout (1)
    • 5.1.4 AI Compute Layout (2
    • 5.1.4 AI Compute Layout (3)
    • 5.1.4 AI Compute Layout (4)
    • 5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (1)
    • 5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (2)
    • 5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (7)
    • 5.1.6 Vehicle Operating System for AI (1)
    • 5.1.6 Vehicle Operating System for AI (2)
    • 5.1.7 Underlying Algorithms for End-to-end Autonomous Driving Solutions (1)
    • 5.1.7 Underlying Algorithms for End-to-end Autonomous Driving Solutions (5)
    • 5.1.8 AI Foundation Model Training Platform: Using 4D Parallel Approach
    • 5.1.9 AI Agent (1)
    • 5.1.9 AI Agent (2)
    • 5.1.9 AI Agent (8)
    • 5.1.9 AI Agent (9)
    • 5.1.10 AI Multimodal Interaction Application Scenarios (1)
    • 5.1.10 AI Multimodal Interaction Application Scenarios (2)
    • 5.1.10 AI Multimodal Interaction Application Scenarios (6)
    • 5.1.11 AI Application in R&D and Production (1)
    • 5.1.11 AI Application in R&D and Production (2)
  • 5.2 NIO
    • 5.2.1 AI Layout
    • 5.2.1 Strategy for AI (1)
    • 5.2.1 Strategy for AI (2)
    • 5.2.1 Strategy for AI (3)
    • 5.2.2 AI Compute Layout (1)
    • 5.2.2 AI Compute Layout (5)
    • 5.2.3 Vehicle Operating System for AI (1)
    • 5.2.3 Vehicle Operating System for AI (2)
    • 5.2.3 Vehicle Operating System for AI (7)
    • 5.2.4 AI-based Autonomous Driving Solutions (1)
    • 5.2.4 AI-based Autonomous Driving Solutions (7)
    • 5.2.5 AI Application in Intelligent Cockpit (1)
    • 5.2.5 AI Application in Intelligent Cockpit (2)
    • 5.2.5 AI Application in Intelligent Cockpit (11)
    • 5.2.5 AI Application in Intelligent Cockpit (12)
  • 5.3 Xpeng
    • 5.3.1 AI Layout
    • 5.3.1 Strategy for AI (1)
    • 5.3.1 Strategy for AI (2)
    • 5.3.1 Strategy for AI (3)
    • 5.3.1 Strategy for AI (4)
    • 5.3.2 AI Data Strategy (1)
    • 5.3.2 AI Data Strategy (2)
    • 5.3.2 AI Data Strategy (3)
    • 5.3.3 AI Compute Layout (1)
    • 5.3.3 AI Compute Layout (2)
    • 5.3.3 AI Compute Layout (8)
    • 5.3.4 Vehicle Operating System for AI (1)
    • 5.3.4 Vehicle Operating System for AI (2)
    • 5.3.4 Vehicle Operating System for AI (3)
    • 5.3.4 Vehicle Operating System for AI (4)
    • 5.3.5 AI-based End-to-end Autonomous Driving Solution (1)
    • 5.3.5 AI-based End-to-end Autonomous Driving Solution (6)
    • 5.3.5 AI-based End-to-end Autonomous Driving Solution (7)
    • 5.3.6 AI Application in Intelligent Cockpit (1)
    • 5.3.6 AI Application in Intelligent Cockpit (2)
    • 5.3.6 AI Application in Intelligent Cockpit (3)
    • 5.3.6 AI Application in Intelligent Cockpit (4)
    • 5.3.6 AI Application in Intelligent Cockpit (5)
  • 5.4 Xiaomi Auto
    • 5.4.1 AI Strategy
    • 5.4.2 AI Data Strategy
    • 5.4.3 AI Compute Layout
    • 5.4.4 Vehicle Operating System for AI (1)
    • 5.4.4 Vehicle Operating System for AI (7)
    • 5.4.4 Vehicle Operating System for AI (8)
    • 5.4.5 AI-based Autonomous Driving Solutions (1)
    • 5.4.5 AI-based Autonomous Driving Solutions (2)
    • 5.4.5 AI-based Autonomous Driving Solutions (3)
    • 5.4.5 AI-based Autonomous Driving Solutions (4)
    • 5.4.6 AI Cockpit (1)
    • 5.4.6 AI Cockpit (6)
  • 5.5 Geely
    • 5.5.1 AI Layout
    • 5.5.1 Strategy for AI (1)
    • 5.5.1 Strategy for AI (2)
    • 5.5.1 Strategy for AI (3)
    • 5.5.1 Strategy for AI (4)
    • 5.5.1 Strategy for AI (5)
    • 5.5.2 AI Data Strategy (1)
    • 5.5.2 AI Data Strategy (2)
    • 5.5.2 AI Data Strategy (7)
    • 5.5.3 AI Compute Layout (1)
    • 5.5.3 AI Compute Layout (2)
    • 5.5.3 AI Compute Layout (3)
    • 5.5.2 AI Data Strategy (4)
    • 5.5.4 Vehicle Operating System for AI (1)
    • 5.5.4 Vehicle Operating System for AI (6)
    • 5.5.5 AI-based Autonomous Driving Solutions (1)
    • 5.5.5 AI-based Autonomous Driving Solutions (2)
    • 5.5.5 AI-based Autonomous Driving Solutions (6)
    • 5.5.6 AI Application in Intelligent Cockpit (1)
    • 5.5.6 AI Application in Intelligent Cockpit (2)
    • 5.5.6 AI Application in Intelligent Cockpit (3)
    • 5.5.6 AI Application in Intelligent Cockpit (4)
    • 5.5.7 AI Chassis (1)
    • 5.5.7 AI Chassis (2)
    • 5.5.8 AI Application Cases in Production, Sales and Service
    • 5.5.9 Xingrui Agent Platform for Production
  • 5.6 BYD
    • 5.6.1 AI Layout
    • 5.6.1 Strategy for AI (1)
    • 5.6.1 Strategy for AI (2)
    • 5.6.1 Strategy for AI (3)
    • 5.6.2 AI Data Strategy (1)
    • 5.6.2 AI Data Strategy (2)
    • 5.6.2 AI Data Strategy (3)
    • 5.6.3 AI Compute Layout
    • 5.6.4 AI-based Vehicle Intelligent Architecture: Xuanji Architecture
    • 5.6.5 AI-based Autonomous Driving Solutions (1)
    • 5.6.5 AI-based Autonomous Driving Solutions (2)
    • 5.6.5 AI-based Autonomous Driving Solutions (3)
    • 5.6.5 AI-based Autonomous Driving Solutions (4)
    • 5.6.6 AI Application in Intelligent Cockpit (1)
    • 5.6.6 AI Application in Intelligent Cockpit (2)
    • 5.6.7 AI-powered Manufacturing
  • 5.7 Changan
    • 5.7.1 Digital Strategy (1)
    • 5.7.1 Digital Strategy (6)
    • 5.7.2 AI-based Vehicle Operating System
    • 5.7.3 AI-based Autonomous Driving Solutions (1)
    • 5.7.3 AI-based Autonomous Driving Solutions (2)
    • 5.7.3 AI-based Autonomous Driving Solutions (3)
    • 5.7.4 AI Application in Intelligent Cockpit (1)
    • 5.7.4 AI Application in Intelligent Cockpit (5)
    • 5.7.5 AI-powered Manufacturing (1)
    • 5.7.5 AI-powered Manufacturing (2)
  • 5.8 BAIC
    • 5.8.1 Intelligent Cockpit AI Agent (1)
    • 5.8.1 Intelligent Cockpit AI Agent (2)
    • 5.8.1 Intelligent Cockpit AI Agent (3)
    • 5.8.2 AI-based Vehicle Operating System
    • 5.8.3 AI Application in Intelligent Cockpit (1)
    • 5.8.3 AI Application in Intelligent Cockpit (7)
    • 5.8.3 AI Application in Intelligent Cockpit (8)
  • 5.9 Great Wall Motor
    • 5.9.1 Strategy for AI
    • 5.9.2 AI Data Strategy (1)
    • 5.9.2 AI Data Strategy (2)
    • 5.9.2 AI Data Strategy (3)
    • 5.9.3 AI Compute Layout (1)
    • 5.9.3 AI Compute Layout (2)
    • 5.9.3 AI Compute Layout (3)
    • 5.9.3 AI Compute Layout (4)
    • 5.9.4 AI-based Vehicle Operating System
    • 5.9.5 AI-based Autonomous Driving Solutions (1)
    • 5.9.5 AI-based Autonomous Driving Solutions (2)
    • 5.9.5 AI-based Autonomous Driving Solutions (3)
    • 5.9.6 AI Application in Intelligent Cockpit (1)
    • 5.9.6 AI Application in Intelligent Cockpit (2)
  • 5.10 Chery
    • 5.10.1 Strategy for AI (1)
    • 5.10.1 Strategy for AI (2)
    • 5.10.1 Strategy for AI (3)
    • 5.10.2 AI Data Strategy
    • 5.10.3 AI-based Autonomous Driving Solutions (1)
    • 5.10.3 AI-based Autonomous Driving Solutions (2)
    • 5.10.3 AI-based Autonomous Driving Solutions (3)
    • 5.10.3 AI-based Autonomous Driving Solutions (4)
    • 5.10.4 AI Application in Intelligent Cockpit (1)
    • 5.10.4 AI Application in Intelligent Cockpit (2)
    • 5.10.4 AI Application in Intelligent Cockpit (3)
  • 5.11 SAIC
    • 5.11.1 Strategy for AI (1)
    • 5.11.1 Strategy for AI (2)
    • 5.11.1 Strategy for AI (3)
    • 5.11.1 Strategy for AI (4)
    • 5.11.2 AI Data Strategy (1)
    • 5.11.2 AI Data Strategy (2)
    • 5.11.2 AI Data Strategy (3)
    • 5.11.2 AI Data Strategy (4)
    • 5.11.3 Vehicle Operating System for AI (1)
    • 5.11.3 Vehicle Operating System for AI (2)
    • 5.11.4 AI-based Autonomous Driving Solutions (1)
    • 5.11.4 AI-based Autonomous Driving Solutions (2)
    • 5.11.5 AI Application in Intelligent Cockpit (1)
    • 5.11.5 AI Application in Intelligent Cockpit (2)