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市场调查报告书
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1704031

中国主机厂AI汽车战略（2025年）

Chinese OEMs' AI-Defined Vehicle Strategy Research Report, 2025
出版日期: | 出版商: ResearchInChina | 英文 420 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

1. AI定义汽车依赖数据、运算能力和模型三大要素的深度结合。

数据是指车辆行驶过程中与外部环境互动时收集的不同类型的资讯。这充当了人工智慧定义车辆的 "燃料" ，为训练和优化演算法提供了基本要素。算力包括云端运算中心和处理资料、执行运算任务的车载AI晶片。它就像智慧汽车的 "引擎" ，决定系统性能的上限。模型是基于人工智慧理论和数学模型，用于处理和分析数据，实现一定智慧功能的各种计算步骤和规则。它充当汽车的 "大脑" ，决定汽车的智慧水平。

OEM 需要同时部署三个要素：在数据方面，我们要建立覆盖全场景的能力；在算力方面，要消除晶片能效瓶颈；在模型方面，实现车云协同推理。最终的AI汽车将依靠这三个要素的深度结合，形成一个自我进化的系统：资料随着使用而变得更加复杂，运算能力越来越高、越来越高效，模型透过训练而变得越来越完善。

2.智慧驾驶AI快速迭代，VLA车型的争夺战将于2025年拉开序幕。

智慧驾驶领域的AI技术将以极快的速度演进迭代，从传统的CNN到BEV+Transformer（2023年）、端到端（2024年）、端到端+VLM（2024年末）、VLA（2025）。VLA标誌着智慧驾驶技术的典范飞跃，从 "感知与判断分离" 走向 "感知、推理、执行一体化" 。

作为传统端对端智慧驾驶的演进，VLA（Vision-Language-Action）模型透过多模态融合（视觉+语言+执行）和思考链推理，解决了当前智慧驾驶系统面临的三大核心问题：全局决策能力、可解释性的突破、泛化表现的突破。

>Li Auto、Xpeng、Geely、Xiaomi均宣布计划从 2025 年开始逐步在其车辆中安装 VLA。其他 OEM 也在跟进 AI 集成，儘管技术路径不同（或相似）。

2025年可能是基于VLA的智慧驾驶解决方案的 "奇点时刻" 。采用 VLA 不仅仅是一次技术升级；它将智慧汽车从单纯的 "工具" 转变为 "代理" 。在这场竞赛中，拥有资料库优势、运算能力优势、以及热门车款的公司很可能在未来十年内掌握汽车产业的话语权。对消费者而言，更人性化的出行体验和更激烈的市场竞争将是2025年中国智慧汽车产业的两大底色。

3、汽车製造商正在加快部署人工智慧并将其应用于车辆的步伐。

从Li Auto AI汽车布局来看，2024年起，Li Auto将进入汽车智慧化的蓬勃发展期。将部署业界首个端到端+VLM双系统智慧驾驶与 "车位到车位" 智慧驾驶，并计画于2025年第三季量产落地下一代自动驾驶架构 "Mind VLA" 。

Li Auto于2021年启动车辆作业系统研发专案。拥有200人的团队，超过10亿元的研发投入，已经完成方案选型、架构设计、落实实施。首批版本将于 2024 年实现量产并应用于汽车。2025年3月，在2025 ZGC Forum Annual Conference上，Li Auto的Li Xiang董事长宣布，Li Auto将开源车载操作系统。Li Auto预计，Halo OS开源后，将为汽车产业每年节省100-200亿元研发投入，进一步加速中国人工智慧汽车的发展。

本报告对中国汽车产业进行了研究分析，介绍了AI定义汽车的概念、与软体定义汽车的区别、AI定义汽车的三大要素、各大主机厂的战略布局等。

目录

第1章 AI定义汽车概述

  • AI定义汽车 VS 软体定义汽车（1）
  • AI定义汽车 VS 软体定义汽车（2）
  • AI定义汽车的三大要素（1）
  • AI定义汽车的三大要素（2） AI正在重塑汽车产业格局
  • AI定义汽车将如何改变交通运输产业
  • AI定义汽车时代的人机协作模型
  • AI定义汽车将推动城市治理模式变革
  • AI定义汽车将加速未来交通的到来
  • AI定义的车辆和解决方案课题

第2章 车企AI基础设施层布局：数据+算力

  • AI定义车辆基础设施层：数据
  • 数据是人工智慧技术的核心原料
  • AI定义车辆基础设施层：云端运算能力
  • AI定义车辆基础设施层：车辆算力

第3章 OEM AI模型层级布局

  • 人工智慧模型在汽车领域的应用概述
  • 车载晶片对基于AI模型的要求
  • 基于人工智慧的模型在车辆作业系统中的应用
  • 人工智慧模型在智慧驾驶的应用
  • 人工智慧模型在智慧座舱及互动中的应用
  • 主机厂AI模型应用总结
  • 供应商基于AI的模型应用总结
  • 中国主流人工智慧基础设施模型总结
  • 人工智慧模型在汽车领域应用的课题与发展趋势

第4章：主机厂如何在研发、生产、销售、服务等领域应用AI

  • AI技术赋能主机厂全链条：研发、生产、销售、服务、供应链管理（1）
  • AI技术赋能主机厂全链条：研发、生产、销售、服务、供应链管理（2）
  • AI技术在研发设计上的应用：SoC的研发设计（一）
  • AI技术在研发设计上的应用：SoC的研发设计（二）
  • AI技术在研发设计上的应用：SoC的研发设计（三）
  • AI技术在研发设计上的应用：SoC的研发设计（四）
  • AI技术在研发设计上的应用：智慧座舱交互
  • 在研究、开发和设计中的应用范例
  • AI技术在汽车生产的应用
  • AI技术在汽车生产上的应用（一）
  • AI技术在汽车生产上的应用（二）
  • AI技术在汽车製造业的应用：主机厂应用案例总结（一）
  • AI技术在汽车生产的应用：主机厂应用案例总结（二）
  • AI技术在销售和服务的应用
  • AI技术在销售与服务领域的应用：OEM应用案例总结
  • 原始设备製造商如何打造自己的 AI 团队 (1)
  • 原始设备製造商如何打造自己的 AI 团队 (2)
  • 主机厂AI团队建立案例（一）
  • 主机厂AI团队建立案例（二）
  • 主机厂AI团队建立案例（三）

第5章 主机厂在AI汽车领域的进展与布局

  • Li Auto
  • NIO
  • Xpeng
  • Xiaomi Auto
  • Geely
  • BYD
  • Changan
  • BAIC
  • Great Wall Motor
  • Chery
  • SAIC
简介目录
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)