Chinese OEMs´ AI-Defined Vehicle Strategy Research Report, 2025
상품코드:1704031
리서치사:ResearchInChina
발행일:2025년 04월
페이지 정보:영문 420 Pages
라이선스 & 가격 (부가세 별도)
한글목차
1. AI 정의 차량은 데이터, 컴퓨팅 파워, 모델이라는 세 가지 주요 요소의 깊은 결합에 의존합니다.
데이터는 차량이 주행하고 외부 환경과 상호 작용할 때 수집되는 다양한 유형의 정보를 말합니다. 이는 AI 정의 차량의 '연료' 역할을 하며, 알고리즘의 학습과 최적화를 위한 기본 자료를 제공합니다. 컴퓨팅 파워에는 데이터를 처리하고 컴퓨팅 작업을 수행하는 클라우드 컴퓨팅 센터와 차량 AI 칩이 포함됩니다. 지능형 자동차의 '엔진' 역할을 하며 시스템 성능의 상한을 결정합니다. 모델이란 AI 이론과 수학 모델을 기반으로 한 다양한 계산 단계와 규칙을 말하며, 데이터를 처리하고 분석하여 특정 지능형 기능을 실현하는 데 사용됩니다. 자동차의 '두뇌' 역할을 하며 지능 수준을 결정합니다.
OEM은 세 가지 요소를 동시에 전개해야 합니다. 데이터 측면에서는 모든 시나리오를 커버할 수 있는 능력을 구축해야 하고, 컴퓨팅 파워 측면에서는 칩의 에너지 효율 병목현상을 해결해야 하며, 모델 측면에서는 차량과 클라우드의 협력 추론을 실현해야 합니다. 세 가지 요소의 깊은 결합에 의존하여 "데이터를 사용할수록 데이터는 더 정교해지고, 컴퓨팅 파워는 더 높고, 더 효율적이며, 모델은 훈련을 통해 개선된다"는 자기 진화 시스템을 형성합니다.
2. 지능형 운전 AI의 빠른 반복에서 2025년 VLA 모델을 둘러싼 경쟁이 시작됩니다.
지능형 주행의 AI 기술은 기존의 CNN에서 BEV+Transformer(2023년), End-to-End(2024년), End-to-End+VLM(2024년 후반), VLA(2025년)로 빠른 속도로 진화하고 반복될 것입니다. 판단의 분리'에서 '지각, 추론, 실행의 통합'으로의 지능형 운전 기술의 패러다임적 도약을 보여줍니다.
VLA(Vision-Language-Action) 모델은 기존의 엔드-투-엔드 지능형 주행의 진화형으로서, 멀티모달 융합(시각+언어+실행)과 사고연쇄 추론을 통해 현재 지능형 주행 시스템이 직면한 3가지 핵심 과제, 즉 세계 의사결정 능력의 비약적 향상, 해석 능력의 비약적 향상, 일반화 성능의 비약적 향상을 해결합니다. 즉, 세계 의사결정 능력, 해석 가능성의 비약적 향상, 범용화 성능의 비약적 향상을 해결합니다.
Li Auto, Xpeng, Geely, Xiaomi는 모두 2025년부터 순차적으로 VLA를 자동차에 탑재할 계획을 발표했습니다. 다른 OEM들도 서로 다른(또는 유사한) 기술 경로를 채택하고 있지만, AI 통합에 뒤처지지 않고 있습니다.
2025년은 VLA 기반 지능형 주행 솔루션의 '특이점'이 될 수 있으며, VLA의 채택은 단순한 기술적 업그레이드가 아니라 지능형 자동차를 단순한 '도구'에서 '에이전트'로 변화시킬 수 있습니다. 이 경쟁에서 데이터베이스, 컴퓨팅 파워의 우위, 인기 차종을 보유한 기업이 향후 10년간 자동차 산업에서 발언권을 갖게 될 것으로 보입니다. 소비자에게는 보다 인간적인 모빌리티 경험과 시장 경쟁의 격화가 2025년 중국 지능형 자동차 산업의 두 가지 배경색이 될 것입니다.
3. 자동차 제조업체들은 AI의 개발과 자동차에 적용하는 속도를 높이고 있습니다.
Li Auto의 AI 정의 차량 레이아웃을 보면 2024년 이후 차량 지능의 호황기에 접어들었습니다. 업계 최초로 엔드 투 엔드 + VLM 듀얼 시스템 지능형 주행과 '주차 공간에서 주차 공간으로' 지능형 주행을 전개하고, 2025년 3분기에는 차세대 자율주행 아키텍처 '마인드 VLA'의 양산 및 구현을 계획하고 있습니다.
Li Auto는 2021년에 차량 운행 시스템 연구개발 프로젝트를 시작했고, 200명의 팀과 10억 위안 이상의 연구개발비를 투입하여 솔루션 선정, 아키텍처 설계, 구현을 완료했습니다. 2025년 3월에 열린 2025 ZGC Forum Annual Conference에서 Li Auto의 Li Xiang 회장은 2025년 3월에 열린 2025 ZGC Forum Annual Conference에서 Li Auto가 차량용 OS를 오픈소스로 전환할 것이라고 발표했습니다. 리오토의 추산에 따르면, 헤일로 OS의 오픈소스화는 중복된 연구개발 투자를 제거함으로써 자동차 업계가 연간 100억-200억 위안의 비용을 절감할 수 있으며, 중국 내 AI 정의 차량 개발을 더욱 가속화할 수 있을 것으로 예상했습니다.
본 보고서는 중국의 자동차 산업에 대해 조사 분석했으며, AI 정의 차량의 개념, 소프트웨어 정의 차량과의 차이점, AI 정의 자동차의 3가지 주요 요소, 주요 OEM의 전략과 레이아웃 등의 정보를 제공합니다.
목차
제1장 AI 정의 차량 개요
AI 정의 차량 vs. 소프트웨어 정의 차량(1)
AI 정의 차량 vs. 소프트웨어 정의 차량(2)
AI 정의 차량의 3개 주요 요소(1)
AI 정의 차량의 3개 주요 요소(2)
AI가 자동차 산업 패턴을 재형성
AI 정의 차량이 가져오는 운송 산업 변화
AI 정의 차량 시대 휴먼 머신 협조 모델
AI 정의 차량이 도시 거버넌스 모델 변화를 촉진
AI 정의 차량이 미래 운송 도래를 가속
AI 정의 차량과 솔루션 과제
제2장 OEM AI인프라층 레이아웃 : 데이터+컴퓨팅 파워
AI 정의 차량 인프라층 : 데이터
데이터는 AI 기술의 핵심이 되는 원재료
AI 정의 차량 인프라층 : 클라우드 컴퓨팅 파워
AI 정의 차량 인프라층 : 차량 컴퓨팅 파워
제3장 OEM AI 모델 레이어 레이아웃
자동차 부문 AI 기반 모델 응용 개요
차량 칩 AI 기반 모델 요건
차량 운영체제 AI 기반 모델 응용
지능형 드라이빙 AI 기반 모델 응용
지능형 콕핏과 인터랙션 AI 기반 모델 응용
OEM AI 기반 모델 응용 요약
공급업체의 AI 기반 모델 응용 요약
중국의 주류 AI 기반 모델 요약
자동차 부문 AI 기반 모델 응용 과제와 개발 동향
제4장 OEM가 연구개발, 생산, 판매, 서비스등의 분야에서 AI를 응용하는 방법
AI 기술이 OEM을 체인 전체에 걸쳐 강화 : 연구개발, 생산, 판매, 서비스, 공급망 관리(1)
AI 기술이 OEM을 체인 전체에 걸쳐 강화 : 연구개발, 생산, 판매, 서비스, 공급망 관리(2)
연구개발 및 설계에서의 AI 기술 응용 : SoC 연구개발 및 설계(1)
연구개발 및 설계에서의 AI 기술 응용 : SoC 연구개발 및 설계(2)
연구개발 및 설계에서의 AI 기술 응용 : SoC 연구개발 및 설계(3)
연구개발 및 설계에서의 AI 기술 응용 : SoC 연구개발 및 설계(4)
연구개발 및 설계에서의 AI 기술 응용 : 지능형 콕핏 인터랙션
연구개발 및 설계에서의 AI 기술 응용 사례
자동차 생산에서의 AI 기술 응용
자동차 생산에서의 AI 기술 응용 사례(1)
자동차 생산에서의 AI 기술 응용 사례(2)
자동차 생산에서의 AI 기술 응용 : OEM 응용 사례 요약(1)
자동차 생산에서의 AI 기술 응용 : OEM 응용 사례 요약(2)
판매 및 서비스에서의 AI 기술 응용
판매 및 서비스에서의 AI 기술 응용 : OEM 적용 사례 요약
OEM가 AI팀을 구축하는 방법(1)
OEM가 AI팀을 구축하는 방법(2)
OEM의 AI팀 구축 사례(1)
OEM의 AI팀 구축 사례(2)
OEM의 AI팀 구축 사례(3)
제5장 OEM의 AI 정의 차량 진척과 레이아웃
Li Auto
NIO
Xpeng
Xiaomi Auto
Geely
BYD
Changan
BAIC
Great Wall Motor
Chery
SAIC
LSH
영문 목차
영문목차
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)
