|
시장보고서
상품코드
2064029
AI 정의 차량(AIDV) OEM 각사의 도입 전략에 관한 조사 리포트(2026년)AI-Defined Vehicle (AIDV) OEMs´ Deployment Strategies Research Report, 2026 |
||||||
AIDV 조사: 22개 자동차 제조사의 도입 전략
ResearchInChina가 발표한 ‘AI 정의 차량(AIDV) OEM 각사의 도입 전략에 관한 조사 리포트(2026년)’는 Li Auto, NIO, XPeng, Xiaomi Auto, Geely 등 22개사의 AI 도입 전략을 분석하고 있습니다. 여기에는 자율주행 및 스마트 콕핏 등의 분야에서 AI 데이터, 클라우드 컴퓨팅 역량, 차량용 컴퓨팅 역량, 자체 개발 칩, AI 운영 체제, AI 기반 모델의 활용은 물론, 차량용 AI 칩의 구성 전략 및 계획도 포함됩니다.
1. 신흥 OEM: 포괄적인 사업 영역이 차량에서 EAI로 확대
현재 중국의 신에너지차 스마트화는 다음 단계로 접어들었으며, AI 기술은 OEM 간 경쟁에서 핵심적인 변수로 부상하고 있습니다.
2026년 6월 초에 개최된 ‘Qualcomm China Automotive Summit’에서 NIO의 창업자인 리빈 씨는 오늘날의 자동차 제조사는 AI 기업이 되어야 하며, 오늘날의 지능형 콕핏은 AI 콕핏으로 변모해야 한다고 분명히 밝혔습니다. NIO는 지능형 콕핏의 진화를 ‘기능형 콕핏’, ‘지능형 콕핏’, ‘인지형 콕핏’의 세 단계로 분류하고 있습니다.
2026년 1월, 리샹 씨는 사내 직원 회의에서 세 가지 주장을 펼쳤습니다. 첫째, 2026년은 AI 업계의 주요 기업을 목표로 하는 기업에게 마지막 호기가 될 것이라는 점. 둘째, L4 레벨 자율주행은 늦어도 2028년까지는 확실히 상용화될 것이라는 점. 셋째, 인프라 모델, 칩, OS, EAI를 동시에 전개할 수 있는 기업은 전 세계에서 3곳 이하에 불과하며, Li Auto는 그중 한 곳이 되기 위해 최선을 다할 것.
2025년 11월, XPeng은 AI Day 행사에서 자사의 포지셔닝을 “물리적 AI 세계에서의 모빌리티 탐험가이자 세계적인 EAI 기업”으로 전면적으로 업그레이드하겠다고 발표했습니다.
2026년 자동차 업계의 유행어 중 하나인 AI 에이전트에 대해는 의심의 여지가 없습니다. 많은 업계 전문가들이 AI에 대한 과도한 기대를 누그러뜨릴 것을 촉구하기 시작했습니다. 그러나 이는 OEM이 AIDV(AI 탑재 차량) 분야의 가장 선도적인 실천자로 부상하는 데에는 영향을 미치지 않습니다. 기존 OEM이나 국제 브랜드와 비교했을 때, 신흥 OEM은 AI 연산 능력, AI 칩, AI 기반 모델, EAI 등의 분야에서 포괄적인 사업을 운영하고 있습니다.
2. 기존의 독립 브랜드: 차량 지능화에 초점을 맞춘 중요한 돌파구
Geely, Great Wall Motor, Changan, BYD, Chery로 대표되는 전통적 독립 브랜드들은 자동차 제조에 대한 깊은 이해를 바탕으로, 암묵적으로 ‘차량 지능화’의 길을 걷기 시작했습니다. 그들은 AI를 단순한 개념에서 구체적인 기술적 혁신으로, 또한 웅장한 비전에서 사용자가 직접 체감할 수 있는 가치로 변화시키고 있습니다.
BYD는 ‘쑤안지(Xuanji)’라고 불리는 차량용 지능형 아키텍처를 발표했습니다. 이는 규모의 경제를 활용하여 포괄적인 기술을 제공하는 것으로, ‘차량용 지능형’ 아키텍처를 통해 전동화와 지능화의 심도 있는 융합을 추진하며, 비용 최적화와 글로벌 적용성을 중시하고 있습니다.
Geely(지리)는 ‘AI 집중화’의 길을 선택하고, 통합된 ‘차량 유니버설 브레인’ 구축에 주력하고 있습니다. 이를 통해 AI 기능을 콕핏, 자율주행, 파워트레인 등의 분야에 깊이 접목시켜, 영역 간 연계와 지능적인 진화를 실현하고자 합니다.
수십년에 걸친 자동차 제조 전문 지식을 보유한 기존 OEM 기업은 차량의 기계 구조, 파워트레인 시스템, 섀시 튜닝, 소재 기술 등 AI 기반 기술에 대한 깊은 이해를 갖추고 있으며, 성숙한 엔지니어링 구현 능력을 보유하고 있습니다. 이들은 AI를 활용하여 자율주행 분야, 콕핏 분야, 파워트레인 분야, 섀시 분야, 커넥티비티 분야를 연계하고, 분야를 아우르는 통합을 실현한 차량 지능형 아키텍처를 구축하고 있습니다.
3. 일부 국제 OEM: 해외 시장에서는 신중한 태도를 유지하고, 중국 시장에서는 협력을 심화
국제적인 자동차 브랜드들은 명확한 두 가지 축으로 구성된 전략을 채택하고 있습니다. 중국 시장과 세계 시장을 겨냥한 병행 개발, 현지 공급망과의 연계 및 핵심 기술의 독자적인 연구개발을 통한 이중 전략의 추진, 그리고 신에너지 차량과 기존 연료 차량의 동시 추진을 통한 이중 성장 전략입니다.
2026년 3월, BMW 그룹 CEO는 세계 시장에서 레벨 3(L3) 프로젝트의 연구개발 우선순위를 일시적으로 낮추겠다고 발표했습니다. 한편, 중국 시장에서 BMW 그룹은 ‘360도 풀체인 AI 전략’을 발표하고, 중국 기술 기업인 Momenta와 제휴하여 중국 시장을 겨냥한 모든 시나리오에 대응하는 지능형 주행 시스템을 공동 개발했습니다. 또한 알리바바의 대규모 AI 언어 모델과 DeepSeek의 심층 사고 능력을 기반으로 ‘BMW 지능형 퍼스널 어시스턴트’가 ‘AI 퍼스널 어시스턴트’로 업그레이드되었습니다.
2026년 1월, 해외 언론은 메르세데스-벤츠가 세계 시장에서 L3 자율주행 시스템 추진 계획을 일시적으로 보류하고, 대신 L2+ 자율주행 시스템에 주력하고 있다고 보도했습니다. 중국 시장에서 메르세데스-벤츠는 2017년부터 Momenta와 협력하여 해당 프로젝트에 대한 투자를 이어가고 있습니다. 두 회사가 공동 개발한 첨단 자율주행 시스템은 배터리식 전기자동차인 CLA, GLC SUV 및 차세대 S-클래스 세단에 탑재되어 있습니다.
폭스바겐 그룹은 ‘In China, for China(중국에서, 중국을 위해)’라는 전략을 채택하고 있습니다. 2026년 하반기부터 CEA(China Electronic Architecture)를 기반으로 하는 신형 차량 모델에는 풀 도메인 AI 에이전트가 순차적으로 탑재될 예정입니다. 폭스바겐과 XPeng이 공동 개발한 첫 번째 모델인 UNYX 08에는 2개의 XPeng Turing AI 칩(총 1,500 TOPS의 연산 능력)이 탑재되어 있습니다. 두 회사의 CEA와 Turing 칩 간의 연동은 앞으로 더 많은 폭스바겐 브랜드 모델에 적용될 예정입니다.
테슬라는 국제적인 자동차 브랜드 중에서도 독특한 존재입니다. 이 회사의 AI 도입 전략은 중국의 신흥 자동차 제조업체들의 전략과 유사하며, “AI가 모든 것을 가능하게 한다”는 명확한 특징을 보여주고 있습니다. 테슬라는 자동차를 ‘바퀴 달린 로봇’, 휴머노이드 로봇을 ‘바퀴 없는 자동차’로 간주하며, 독자적인 칩과 AI 기반 모델을 개발함으로써 자동차와 휴머노이드 로봇이 동일한 AI 기술 스택을 공유할 수 있도록 하고 있습니다.
목차
제1장 AI 정의 차량의 개요
제2장 AI 기본 전략과 구성 : 데이터＋계산 능력
제3장 AI 모델의 전략과 구성
제5장 인공 일반 지능 레벨 : 주요 응용상 과제
제4장 AI 칩의 전략과 레이아웃
제5장 AI 정의 차량에서 OEM 각사의 진척 상황과 배포
KSAAIDV Research: Deployment Strategies of 22 OEMs
The AI-Defined Vehicle (AIDV) OEMs' Deployment Strategies Research Report, 2026, released by ResearchInChina, analyzes the AI deployment strategies of 22 OEMs such as Li Auto, NIO, XPeng, Xiaomi Auto, and Geely, involving the application of AI data, cloud computing power, automotive computing power, self-developed chips, AI operating systems and AI foundation models in intelligent driving, intelligent cockpits, and other fields, as well as automotive AI chip configuration strategies and planning.
1. Emerging OEMs: Comprehensive deployment expands from vehicles to EAI
Currently, the intelligence of China's new energy vehicles is entering the next stage, and AI technology has become a core variable in the competition among OEMs.
At the Qualcomm China Automotive Summit in early June 2026, NIO founder Li Bin clearly stated that today's automotive companies must become AI companies, and today's intelligent cockpits must turn into AI cockpits. NIO divides the intelligent cockpit evolution into three stages: "functional cockpits", "intelligent cockpits" and "cognitive cockpits".
In January 2026, Li Xiang made three assertions at an internal staff meeting: First, 2026 marks the last window of opportunity for enterprises aiming to become top players in the AI industry; second, L4 intelligent driving will definitely be applied as late as 2028; third, there will be no more than three companies in the world that can deploy foundation models, chips, operating systems, and EAI at the same time, and Li Auto will strive to become one of them.
In November 2025, XPeng announced at the AI Day that it would comprehensively upgrade its positioning to "a mobility explorer in the physical AI world and a global EAI company".
There is no doubt that AI agents, one of the buzzwords in the automotive industry in 2026. Many industry experts have started to call for cooling down the AI hype. But this does not affect the emergence of OEMs as the most radical practitioners of AIDVs. Compared with traditional OEMs and international brands, emerging OEMs have made a comprehensive layout in the fields of AI computing power, AI chips, AI foundation models, EAI and other fields.
2. Traditional Independent Brands: Key Breakthroughs Focus on Vehicle Intelligence
Traditional independent brands represented by Geely, Great Wall Motor, Changan, BYD, and Chery are relying on their deep understanding of vehicle manufacturing to tacitly embark on a "vehicle intelligence" path. They have transformed AI from a concept into a concrete technological breakthrough, and from a grand vision into perceived user value.
BYD has released a vehicle intelligent architecture called "Xuanji", which relies on scale effects to offer inclusive technology. It uses the "vehicle intelligent" architecture to promote the deep integration of electrification and intelligence, emphasizing cost optimization and global adaptability.
Geely has chosen the "AI centralization" path and is committed to building a unified "vehicle universal cerebrum" to deeply penetrate AI capabilities into cockpit, intelligent driving, powertrain and other fields to achieve cross-domain collaboration and intelligent evolution.
With decades of expertise in vehicle manufacturing, traditional OEMs have a deep understanding of vehicle mechanical structure, powertrain system, chassis tuning, material technology and other AI carriers, and mature engineering implementation capabilities. They use AI to link the intelligent driving domain, cockpit domain, powertrain domain, chassis domain and connectivity domain to achieve a vehicle intelligent architecture with cross-domain integration.
3. Some International OEMs: Stay Cautious in Overseas Markets and Deepen Cooperation in the Chinese Market
International auto brands have adopted distinct two-pronged strategies: dual-track parallel development for the Chinese market and global markets; dual-line progress through cooperation with local supply chains and independent R&D of core technologies; and two-wheel driven growth by promoting new energy vehicles and traditional fuel vehicles simultaneously.
In March 2026, the CEO of the BMW Group announced that it would temporarily lower the R&D priority of the L3 project in the global market; at the same time, in the Chinese market, the BMW Group released a "360-degree full-chain AI strategy" and joined hands with the Chinese technology company Momenta to jointly develop an all-scenario intelligent driving system for the Chinese market. Besides, based on Alibaba's AI large language model and DeepSeek's deep thinking capabilities, the BMW Intelligent Personal Assistant was upgraded to an "AI Personal Assistant";
In January 2026, overseas media reported that Mercedes-Benz had temporarily shelved its L3 intelligent driving system promotion plan in the global market and instead focused on the L2+ intelligent driving system. In the Chinese market, Mercedes-Benz has continued to invest in the project in cooperation with Momenta since 2017. The high-level intelligent driving system developed by the two parties has been implemented in the battery-electric CLA, GLC SUV and next-generation S-class sedan.
The Volkswagen Group adopts the "In China, for China" strategy. Starting from the second half of 2026. New vehicle models based on the CEA (China Electronic Architecture) will gradually be equipped with full-domain AI agents. The first model CO-developed by Volkswagen and XPeng, UNYX 08, is equipped with two XPeng Turing AI chips (totaling 1500 TOPS of computing power). Their cooperative CEA and Turing chips will be applied to more Volkswagen-branded models in the future.
Tesla is an outlier among international automotive brands. Its AI deployment strategy is more similar to that of emerging OEMs in China, exhibiting a distinct characteristic of "AI enables everything". Tesla sees cars as "wheeled robots" and humanoid robots as "wheelless cars", and develops its own chips and AI foundation models, making cars and EAI share the same AI technology stack.
Table of Contents
Definitions
1 Overview of AI-Defined Vehicles
- 1.1 Overview of AI-Defined Vehicles
- AI-Defined Vehicles vs. Software-Defined Vehicles (1)
- AI-Defined Vehicles vs. Software-Defined Vehicles (2)
- Three Key Elements of AI-Defined Vehicles (1)
- Three Key Elements of AI-Defined Vehicles (2)
- AI Is Reshaping the Automotive Industry Pattern
- AI Technology Empowers OEMs Across the Entire Chain: R&D, Production, Sales, Service, and Supply Chain Management
- Application of AI Technology in Vehicle Production
- Application of AI Technology in Vehicle Production: Summary of OEMs' Applications (1)
- Application of AI Technology in Vehicle Production: Summary of OEMs' Applications (2)
- Application of AI Technology in Sales and Service
- Application of AI Technology in Sales and Service: Summary of OEMs' Applications
- 1.2 AIDV Data Statistics
- AI-powered Autonomous Driving: Installation Volume and Penetration Rate in 2025
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by Price Range)
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by Vehicle Class)
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by New Energy Vehicle Type)
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by OEM)
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by Brand)
- AI-powered Autonomous Driving: Penetration Rate in 2025 (by Vehicle Model)
- AI-powered MNP: Installation Volume and Penetration Rate in 2025
- AI-powered MNP: Penetration Rate in 2025 (by Price Range)
- AI-powered MNP: Penetration Rate in 2025 (by Vehicle Class)
- AI-powered MNP: Penetration Rate in 2025 (by New Energy Vehicle Type)
- AI-powered MNP: Penetration rate in 2025 (by OEM, Brand and Vehicle Model)
- AI-powered Parking: Installation Volume and Penetration Rate in 2025
- AI-powered Parking: Penetration Rate in 2025 (by Price Range)
- AI-powered Parking: Penetration Rate in 2025 (by Vehicle Class)
- AI-powered Parking: Penetration Rate in 2025 (by New Energy Vehicle Type)
- AI-powered Parking: Penetration rate in 2025 (by OEM, Brand and Vehicle Model)
- AI-powered Voice Assistant: Installation Volume and Penetration Rate in 2025
- AI-powered Voice Assistant: Penetration Rate in 2025 (by Price Range)
- AI-powered Voice Assistant Penetration Rate in 2025 (by Vehicle Class)
- AI-powered Voice Assistant: Penetration Rate in 2025 (by New Energy Vehicle Type)
- AI-powered Voice Assistant: Penetration Rate in 2025 (by OEM)
- AI-powered Voice Assistant: Penetration Rate in 2025 (by Brand)
- AI-powered Voice Assistant: Penetration Rate in 2025 (by Vehicle Model)
- AI Automotive Foundation Models: Installation Volume and Penetration Rate in 2025
- AI Automotive Foundation Models: Penetration Rate in 2025 (by Price Range)
- AI Automotive Foundation Models: Penetration Rate in 2025 (by Vehicle Class)
- AI Automotive Foundation Models: Penetration Rate in 2025 (by New Energy Vehicle Type)
- AI Automotive Foundation Models: Penetration Rate in 2025 (by OEM)
- AI Automotive Foundation Models: Penetration Rate in 2025 (by Brand)
- AI Automotive Foundation Models: Penetration Rate in 2025 (by Vehicle Model)
- 1.3 Challenges in AI-Defined Vehicles
- Challenges in AI-Defined Vehicles: Technical Difficulties and Solutions (1)
- Challenges in AI-Defined Vehicles: Technical Difficulties and Solutions (2)
- Challenges in AI-Defined Vehicles: Social Ethics
- Challenges in AI-Defined Vehicles: Industry Standards
- Challenges in AI-Defined Vehicles: Laws and Regulations (1)
- Challenges in AI-Defined Vehicles: Laws and Regulations (2)
- Challenges in AI-Defined Vehicles: Challenges in Deploying Automotive AI Foundation Models
- 1.4 AI Patents of OEMs
- Geely - Multi-Agent Collaborative Vehicle Control Solution
- Geely: An Autonomous Driving Method Based on Multi-Modal Memory Assistance
- Geely: An Automotive Model Deployment Framework and Model Access Method
- GAC Group: An Agent-Based Vehicle Computing Task Scheduling Solution
- GAC Group: A Multi-Agent Collaborative Control Solution for "Cockpit-Driving Integration"
- Great Wall Motor: An Agent-Based Active Service Vehicle Control Solution
- Chery: A Multi-Agent Collaboration Solution Based on Foundation Model Intent Recognition
- Changan Automobile: A Multi-Autonomous-Vehicle Collaborative Decision-Making Solution
- Changan Automobile: An Autonomous Driving System and Its Autonomous Learning Method
- FAW Group: A Vehicle Fault Analysis Solution Based on an AI Agent
- FAW Group: A Two-Way Development Method for Automotive AI and Users
- Leapmotor: A Dynamic Optimization Method for Perception Task Models Based on Benchmark Models and Reinforcement Learning Agents
- Voyah: A Solution to Improve the Response Speed ??of Voice Control in Intelligent Cockpits
- 1.5 Development Trends of AI-Defined Vehicles
- Trend 1: Explainable AI Large Models Are Being Applied to Vehicles (1)
- Trend 1: Explainable AI Large Models Are Being Applied to Vehicles (2)
- Trend 1: Explainable AI Large Models Are Being Applied to Vehicles (3)
- Trend 2: Automotive AI Applications Increase Generalization Capabilities
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (1)
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (2)
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (3)
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (4)
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (5)
- Trend 3: Introduction of AI BOX in Vehicles Accelerates Vehicle Intelligence (6)
- Trend 4: AI Integrates with Vehicle Operating Systems (1)
- Trend 4: AI Integrates with Vehicle Operating Systems (2)
- Trend 4: AI Integrates with Vehicle Operating Systems (3)
- Trend 4: AI Integrates with Vehicle Operating Systems (4)
- Trend 4: AI Integrates with Vehicle Operating Systems (5)
- Trend 5: New Technologies Lower End-side Computing Power Requirements
- Trend 6: World Models Upgrade from Auxiliary Tools to Core Foundation (1)
- Trend 6: World Models Upgrade from Auxiliary Tools to Core Foundation (2)
- Trend 7: VLA and World Models Converge toward Physical AI (1)
- Trend 7: VLA and World Models Converge toward Physical AI (2)
- Trend 7: VLA and World Models Converge toward Physical AI (3)
- Trend 7: VLA and World Models Converge toward Physical AI (4)
- Trend 7: VLA and World Models Converge toward Physical AI (5)
2 AI Basic Strategies and Layout: Data + Computing Power
- 2.1 AI-Defined Vehicles: Data Strategies
- AI Applications in Vehicle Data Collection, Transmission, and Storage
- AI Applications in Vehicle Data Processing, Annotation, and Training
- Summary of OEMs' Cloud Platform Cooperation (1)
- Summary of OEMs' Cloud Platform Cooperation (2)
- Summary of OEMs' Cloud Platform Cooperation (3)
- Summary of Cloud Native Application by OEMs (1)
- Summary of Cloud Native Application by OEMs (2)
- Distribution of AI Data Centers (1)
- Distribution of AI Data Centers (2)
- Public Cloud Data Center Layout
- Summary of AI Data Application by Suppliers (1)
- Summary of AI Data Application by Suppliers (2)
- Summary of AI Data Application by Suppliers (3)
- Summary of AI Data Application by Suppliers (4)
- 2.2 AI-Defined Vehicles: Computing Power Strategies
- Requirements for Cloud Computing Power in AI Technology Applications and Solutions
- How OEMs Build Cloud Computing Power Required by AI (1)
- How OEMs Build Cloud Computing Power Required by AI (2)
- Summary of Cloud Computing Power Platforms of Domestic OEMs (1)
- Summary of Cloud Computing Power Platforms of Domestic OEMs (2)
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (1): Li Auto
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (2): XPeng
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (3): NIO
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (4): Xiaomi Auto, Leapmotor
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (5): BYD
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (6): Changan, BAIC
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (7): Great Wall Motor, SAIC
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (8): Chery
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (9): Geely
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (10): Dongfeng Motor, GAC
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (11): Tesla, Audi
- Automotive Computing Power Configuration of Typical Vehicle Models of OEMs (12): Toyota, Nissan
- Autonomous Vehicle Computing Power Token Billing Modes (1)
- Autonomous Vehicle Computing Power Token Billing Modes (1)
- Requirements for Vehicle Computing Power in AI Technology Applications and Solutions
- How OEMs Build Vehicle Computing Power Required by AI
3 AI Model Strategies and Layout
- 3.1 Overview of Foundation Model Applications in the Automotive Industry
- Definition and Features of AI Foundation Models
- Classification of AI Foundation Models and Their Applications in the Automotive Sector
- Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (1)
- Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (2)
- Invocation Modes of Automotive Device Functions by AI Foundation Models (1)
- Invocation Modes of Automotive Device Functions by AI Foundation Models (2)
- Summary of AI Foundation Model Applications of OEMs
- Summary of AI Foundation Model Applications of Suppliers
- Summary of Mainstream AI Foundation Models in China
- 3.2 Application of AI Foundation Models in Automotive Operating Systems
- Impacts of AI Foundation Models on Vehicle Operating Systems
- AI Foundation Models Can Be Used to Generate AUTOSAR Tests
- Application of AI Foundation Models in Vehicle Operating Systems (1)
- Application of AI Foundation Models in Vehicle Operating Systems (2)
- Application of AI Foundation Models in Vehicle Operating Systems (3)
- Application of AI Foundation Models in Vehicle Operating Systems (4)
- Application of AI Foundation Models in Vehicle Operating Systems (5)
- AIOS Architecture: Main Components and Functions of Kernel Module (1)
- AIOS Architecture: Main Components and Functions of Kernel Module (2)
- AIOS Architecture: Main Components and Functions of Kernel Module (3)
- AIOS Architecture: Main Components and Functions of Kernel Module (4)
- AIOS Architecture: Main Components and Functions of Kernel Module (5)
- AIOS Architecture: Main Components and Functions of Kernel Module (6)
- AIOS Architecture: AI Foundation Model Deployment and Task Flow
- AIOS Architecture: Comparison between Different AI Runtimes
- 3.3 Application of AI Foundation Models in Autonomous Driving
- Application of AI Foundation Models in Intelligent Driving
- Generative Simulation Technology for AI Foundation Models Enhances Capabilities of Driving Simulation Systems
- Application of AI Foundation Models in Intelligent Driving Perception (1)
- Application of AI Foundation Models in Intelligent Driving Perception (2)
- Application of AI Foundation Models in Intelligent Driving Decision (1)
- Application of AI Foundation Models in Intelligent Driving Decision (2)
- Trends in Application of AI Foundation Models in Intelligent Driving (1)
- Trends in Application of AI Foundation Models in Intelligent Driving (2)
- Trends in Application of AI Foundation Models in Intelligent Driving (3)
- AI Deployment Strategies for Autonomous Driving of OEMs (1)
- AI Deployment Strategies for Autonomous Driving of OEMs (2)
- AI Deployment Strategies for Autonomous Driving of OEMs (3)
- AI Deployment Strategies for Autonomous Driving of OEMs (4)
- AI Deployment Strategies for Autonomous Driving of OEMs (5)
- AI Deployment Strategies for Autonomous Driving of OEMs (6)
- AI Deployment Strategies for Autonomous Driving of OEMs (7)
- AI Deployment Strategies for Autonomous Driving of OEMs (8)
- AI Deployment Strategies for Autonomous Driving of OEMs (9)
- AI Deployment Strategies for Autonomous Driving of OEMs (10)
- AI Deployment Strategies for Autonomous Driving of OEMs (11)
- AI Deployment Strategies for Autonomous Driving of OEMs (12)
- AI Deployment Strategies for Autonomous Driving of OEMs (13)
- AI Deployment Strategies for Autonomous Driving of OEMs (14)
- AI Deployment Strategies for Autonomous Driving of OEMs (15)
- AI Deployment Strategies for Autonomous Driving of OEMs (16)
- AI Deployment Strategies for Autonomous Driving of OEMs (17)
- 3.4 Application of AI Foundation Models in Smart Cockpits
- Application of AI Foundation Models in Intelligent Cockpit: AI-Defined Cockpit vs. Software-Defined Cockpit
- Application Scenarios of AI Foundation Models in Intelligent Cockpit
- AI Foundation Models Deployed In Smart Cockpits Have Become Standard
- Deployment of AI Foundation Models in Smart Cockpits Features Edge-First, Edge-Cloud Collaboration
- Application of AI Foundation Models in HUD
- Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (1)
- Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (2)
- Application of AI Foundation Models in Intelligent Cockpit Gesture Recognition
- Application of AI Foundation Models in DMS (1)
- Application of AI Foundation Models in DMS (2)
- Application of AI Foundation Models in Intelligent Cockpit Personalized Services
- Trends in Application of AI Foundation Models in Intelligent Cockpit (1)
- Trends in Application of AI Foundation Models in Intelligent Cockpit (2)
- Trends in Application of AI Foundation Models in Intelligent Cockpit (3)
- AI Deployment Strategies for Smart Cockpits of OEMs (1)
- AI Deployment Strategies for Smart Cockpits of OEMs (2)
- AI Deployment Strategies for Smart Cockpits of OEMs (3)
- AI Deployment Strategies for Smart Cockpits of OEMs (4)
- AI Deployment Strategies for Smart Cockpits of OEMs (5)
- AI Deployment Strategies for Smart Cockpits of OEMs (6)
- AI Deployment Strategies for Smart Cockpits of OEMs (7)
- AI Deployment Strategies for Smart Cockpits of OEMs (8)
- AI Deployment Strategies for Smart Cockpits of OEMs (9)
- AI Deployment Strategies for Smart Cockpits of OEMs (10)
- AI Deployment Strategies for Smart Cockpits of OEMs (11)
- AI Deployment Strategies for Smart Cockpits of OEMs (12)
- 3.5 Application of AI Agent in Intelligent Vehicles
- Summary of AI Agent Deployment in Vehicles by OEMs (1)
- Summary of AI Agent Deployment in Vehicles by OEMs (2)
- Summary of AI Agent Deployment in Vehicles by OEMs (3)
5 Levels of AGI: Main Application Issues
- Four Stages of Cockpit L3 Agent Iteration
- Cockpit Agent Is the Foundation for Generating User Value
- Cockpit Agent: Popularization of Device-cloud Architecture
- Cockpit Agent: On-device AI Is More Suitable for Specific Tasks
- Cockpit Agent: Foundation Models Further Advances Cockpit-Driving Integration
- Cockpit Agent: Different Development Modes of OEMs
- Cockpit Agent: Multimodal / Omnimodal
- Cockpit Agent: Typical Multimodal Architecture (1)
- Cockpit Agent: Typical Multimodal Architecture (2)
- Agents with Emergent Capabilities: Training Methods of Interactive Agents
- Agents with Emergent Capabilities: Sim-to-real
4 AI Chip Strategies and Layout
- 4.1 Chip Strategies for Edge AI Deployment
- Edge AI Deployment: High-Compute, Low-Power Computing-in-Memory Chips
- Edge AI Deployment: Distillation Can Lower Vehicle Computing Power Requirements
- Edge AI Deployment: Application Scenarios of AI Smart Cockpits
- Edge AI Deployment: Autonomous Driving AI Computing Power Allocation Mechanism
- Chip Vendors Fulfill Deep Edge AI Optimization Through Multiple Paths
- Edge Foundation Models Drive Memory Chip Upgrades, And LPDDR6 Becomes the Next-Generation Technology Focus
- Edge AI Deployment: Mainstream Chips and AI Computing Power (1)
- Edge AI Deployment: Mainstream Chips and AI Computing Power (2)
- Challenges for Edge AI Deployment (1): Computing Power
- Challenges for Edge AI Deployment (2): Storage (1)
- Challenges for Edge AI Deployment (2): Storage (2)
- Challenges for Edge AI Deployment (3): DRAM Storage Bandwidth in Mass Production and Deployment (1)
- Challenges for Edge AI Deployment (3): DRAM Storage Bandwidth in Mass Production and Deployment (2)
- Challenges for Edge AI Deployment (3): DRAM Storage Bandwidth in Mass Production and Deployment (3)
- Challenges for Edge AI Deployment (3): DRAM Storage Bandwidth in Mass Production and Deployment (4)
- Challenges for Edge AI Deployment (3): DRAM Storage Bandwidth in Mass Production and Deployment (5)
- Innovative Strategies for Edge AI Deployment (1): AI Agents Deployed in Vehicles
- Innovative Strategies for Edge AI Deployment (2): AI Box (1)
- Innovative Strategies for Edge AI Deployment (2): AI Box (2)
- Innovative Strategies for Edge AI Deployment (3): Deployment Modes of AI Box in Vehicles
- Innovative Strategies for Edge AI Deployment (4): Comparison between Some OEM AI Box Products (1)
- Innovative Strategies for Edge AI Deployment (4): Comparison of Some OEM AI Box Products (2)
- Edge AI Deployment Strategies of OEMs (1): Foundation Models Are Compressed for Vehicle Deployment to Achieve Edge-Cloud Integration (1)
- Edge AI Deployment Strategies of OEMs (1): Foundation Models Are Compressed for Vehicle Deployment to Achieve Edge-Cloud Integration(2)
- Edge AI Deployment Strategies of OEMs (2): Cross-Domain Integration SoC Performance Iteration Accelerates (1)
- Edge AI Deployment Strategies of OEMs (2): Cross-Domain Integration SoC Performance Iteration Accelerates (2)
- 4.2 Typical Chip Products That Support Intelligent Driving AI
- Summary of High-compute Chip Products That Support AI, and Automotive Application Cooperation (1)
- Summary of High-compute Chip Products That Support AI, and Automotive Application Cooperation (2)
- Summary of High-compute Chip Products That Support AI, and Automotive Application Cooperation (3)
- Comparison of Typical High-compute Chip Products That Support AI (1): NVIDIA Thor-X
- Comparison of Typical High-compute Chip Products That Support AI (2): NVIDIA Thor-U
- Comparison of Typical High-compute Chip Products That Support AI (3): Qualcomm 8797
- Comparison of Typical High-compute Chip Products That Support AI (4): Horizon J6P
- Comparison of Typical High-compute Chip Products That Support AI (5): Xpeng "Turing" AI chip
- Comparison of Typical High-compute Chip Products That Support AI (6): NIO Shenji NX9031
- Comparison of Typical High-compute Chip Products That Support AI (7): Rhino Guangzhi R1
- Comparison of Typical High-compute Chip Products That Support AI (8): SiEngine "Xingchen No.1" (AD1000)
- Comparison of Typical High-compute Chip Products That Support AI(9): Ambarella CV3-AD685
- Comparison of Typical High-compute Chip Products That Support AI (10): Nvidia Orin-Y
- Comparison of Typical High-compute Chip Products That Support AI (11): NVIDIA Orin-X
- Comparison of Typical High-compute Chip Products That Support AI (12): Tesla HW 4.0 Gen 2 FSD
- Comparison of Typical High-compute Chip Products That Support AI (13): Renesas R-Car X5H
- Comparison of Typical High-compute Chip Products That Support AI (14): Huawei Ascend 610
- Comparison of Typical High-compute Chip Products That Support AI (15): Black Sesame A2000
- 4.3 Typical Chip Products That Support Intelligent Cockpit AI
- Summary of Chip Products That Support Intelligent Cockpit AI
- Comparison of Chip Products That Support Intelligent Cockpit AI (1): SemiDrive X10, Samsung V920, MediaTek MT8676 (1)
- Comparison of Chip Products That Support Intelligent Cockpit AI (1): SemiDrive X10, Samsung V920, MediaTek MT8676 (2)
- Comparison of Chip Products That Support Intelligent Cockpit AI (2): Qualcomm 8397, Intel Panther Lake Automotive Edition, Qualcomm SA8775P (1)
- Comparison of Chip Products That Support Intelligent Cockpit AI (2): Qualcomm 8397, Intel Panther Lake Automotive Edition, Qualcomm SA8775P (2)
- Comparison of Chip Products That Support Intelligent Cockpit AI (3): Renesas R-Car X5H, NVIDIA Thor, Qualcomm SA8795P (1)
- Comparison of Chip Products That Support Intelligent Cockpit AI (3): Renesas R-Car X5H, NVIDIA Thor, Qualcomm SA8795P (2)
- Comparison of Chip Products That Support Intelligent Cockpit AI (4): MediaTek C-X1/CT-Y1, Qualcomm 8797 (1)
- Comparison of Chip Products That Support Intelligent Cockpit AI (4): MediaTek C-X1/CT-Y1, Qualcomm 8797 (2)
- Comparison of Chip Products That Support Intelligent Cockpit AI (5): MediaTek Dimensity P1 Ultra/S1 Ultra, CT-X1 (1)
- Comparison of Chip Products That Support Intelligent Cockpit AI (5): MediaTek Dimensity P1 Ultra/S1 Ultra, CT-X1 (2)
- 4.4 Analysis of Intelligent Vehicle AI Chip Costs
- Composition of Intelligent Vehicle AI Chip Costs (1)
- Composition of Intelligent Vehicle AI Chip Costs (2)
- Composition of Intelligent Vehicle AI Chip Costs (3)
- Composition of Intelligent Vehicle AI Chip Costs (4): Tape-out Cost
- Intelligent Vehicle AI Chip Shipping Price Estimate (1)
- Intelligent Vehicle AI Chip Shipping Price Estimate (2)
- Intelligent Vehicle AI Chip Shipping Price Estimate (3)
- Intelligent Vehicle AI Chip Shipping Price Estimate (4)
- Intelligent Vehicle AI Chip Shipping Price Estimate (5)
- 4.5 OEMs' Automotive Chip Configuration Strategies and Planning
- OEMs' Automotive Chip Configuration Strategies and Planning (1): NIO, XPeng, Leapmotor
- OEMs' Automotive Chip Configuration Strategies and Planning (2): Li Auto, GAC Group
- OEMs' Automotive Chip Configuration Strategies and Planning (3): FAW, SAIC
- OEMs' Automotive Chip Configuration Strategies and Planning (4): BAIC Group, Changan
- OEMs' Automotive Chip Configuration Strategies and Planning (5): Great Wall Motor, Dongfeng Motor
- OEMs' Automotive Chip Configuration Strategies and Planning (6): Geely, Xiaomi Auto
- OEMs' Automotive Chip Configuration Strategies and Planning (7): Chery, Tesla
- OEMs' Automotive Chip Configuration Strategies and Planning (8): BYD, BMW
- OEMs' Automotive Chip Configuration Strategies and Planning (9): Volkswagen, Audi, Mercedes-Benz
5 OEMs' Progress and Layout in AI-Defined Vehicles
- 5.1 Li Auto
- AI Strategy (1)
- AI Strategy (2)
- AI Data Strategy
- AI Computing Strategy (1): Cloud Computing
- AI Computing Strategy (2): Edge Computing
- AI Chip Strategy (1): Self-developed Chip M100
- AI Chip Strategy (2): Chip Cooperation
- AI Deployment Strategies for Vehicle Operating Systems (1): Li OS
- AI Deployment Strategies for Vehicle Operating Systems (2): Halo OS (1)
- AI Deployment Strategies for Vehicle Operating Systems (2): Halo OS (2)
- AI Deployment in Intelligent Driving (1): AI Large Model Iteration
- AI Deployment in Intelligent Driving (2): Five Major Technological Innovations of MindVLA-o1
- AI Deployment in Intelligent Driving (2): 3D Vision Model of MindVLA-o1
- AI Deployment in Intelligent Driving (2): Predictive Latent World Model of MindVLA-o1
- AI Deployment in Intelligent Driving (2): Action Architecture of MindVLA-o1 Large Model
- AI Deployment in Intelligent Driving (2): Iteration Mode of MindVLA-o1 Large Model
- AI Deployment in Intelligent Driving (3): Architecture of MindVLA-U1 Large Model
- AI Deployment in Intelligent Driving (4): Physical AI
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (2): Mind GPT Architecture and Technical Features
- AI Deployment in Intelligent Cockpits (2): Multimodal Perceptive Interaction Design Empowered by MindGPT
- AI Deployment in Intelligent Cockpits (3): Lixiang Tongxue Evolves into A Life Assistant Agent
- AI Deployment in Intelligent Cockpits (4): Real-time Voice Dialogue Large Model MindGPT-4o-Audio
- 5.2 NIO
- AI Strategy
- AI Data Strategy
- AI Computing Strategy (1): Cloud Computing
- AI Computing Strategy (2): Edge Computing
- AI Chip Strategy (1): Self-developed Chip Shenji NX9031
- AI Chip Strategy (2): Chip Cooperation
- AI Deployment Strategies for Operating Systems: SkyOS (1)
- AI Deployment Strategies for Operating Systems: SkyOS (2)
- AI Deployment in Intelligent Driving(1): Iteration of AI Large Models
- AI Deployment in Intelligent Driving (2): Iterative Development of AI Foundation Model Intelligent Driving System
- AI Deployment in Intelligent Driving (3): NADArch 2.0 Architecture
- AI Deployment in Intelligent Driving (4): NIO World Model (1)
- AI Deployment in Intelligent Driving (4): NIO World Model (2)
- AI Deployment in Intelligent Driving (4): NIO World Model (3)
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (2): Banyan 3
- AI Deployment in Intelligent Cockpits (3): NOMI GP
- AI Deployment in Intelligent Cockpits (4): AI Emotional Engine
- AI Deployment in Intelligent Cockpits (5): A-VL Technology
- 5.3 Xpeng
- AI Strategy: From Smart Cars to Physical AI
- AI Data Strategy
- AI Computing Power Strategy (1): Cloud Computing Power
- AI Computing Power Strategy (2): Edge Computing Power
- AI Chip Strategy (1): Self-Developed "Turing" Chip
- AI Chip Strategy (2): Performance Parameters of Turing Chip
- AI Chip Strategy (3): Chip Collaboration
- AI Chip Strategy (4): Turing Chip First Installed in Xpeng G7 in 2025
- AI Deployment Strategies for Operating Systems (1): Tianji AIOS
- AI Deployment Strategies for Operating Systems (2): XOS Development Plan
- AI Deployment Strategies for Operating Systems (3): Tianji System Is Upgraded to AIOS
- AI Deployment Strategies for Operating Systems (4): Functional Modules of AI OS
- AI Deployment in Intelligent Driving (1): Iteration of AI Large Models
- AI Deployment in Intelligent Driving (2): Xnet, Xplanner, XBrain
- AI Deployment in Intelligent Driving (3): AI+XNGP
- AI Deployment in Intelligent Driving (4): World Foundation Model "Cloud Model Factory"
- AI Deployment in Intelligent Driving (5): Second Generation VLA (1)
- AI Deployment in Intelligent Driving (5): Second Generation VLA (2)
- AI Deployment in Intelligent Driving (6): Fast Drive VLA Model (1)
- AI Deployment in Intelligent Driving (6): Fast Drive VLA Model (2)
- AI Deployment in Intelligent Driving (7): World Model X-World (1)
- AI Deployment in Intelligent Driving (7): World Model X-World (2)
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (2): Evolution of Intelligent Cockpit System
- AI Deployment in Intelligent Cockpits (3): Core Functions of AI Cockpit
- AI Deployment in Intelligent Cockpits (4): AI Cockpit-Driving Integration
- AI Deployment in Intelligent Cockpits (5): AIOS 6.0
- AI Deployment in Intelligent Cockpits (6): XPeng VLM
- 5.4 Xiaomi
- AI Strategy: Focus on Deploying Infrastructure Including AI Large Models
- AI Data Strategy
- AI Computing Power Strategy: Edge and Cloud Computing Power
- AI Chip Strategy: Chip Cooperation
- AI Deployment Strategies for Operating Systems (1): HyperOS (1)
- AI Deployment Strategies for Operating Systems (1): HyperOS (2)
- AI Deployment Strategies for Operating Systems (2): Accessing DeepSeek R1
- AI Deployment Strategies for Operating Systems (3): HyperOS 2.0
- AI Deployment in Intelligent Driving (1): Iteration of AI Large Models
- AI Deployment in Intelligent Driving (2): Xiaomi Pilot
- AI Deployment in Intelligent Driving (3): MiLM
- AI Deployment in Intelligent Driving (4): Vision Language Model (VLM) (1)
- AI Deployment in Intelligent Driving (4): Vision-Language Model (VLM) (2)
- AI Deployment in Intelligent Driving (4): Vision-Language Model (VLM) (3)
- AI Deployment in Intelligent Driving (5): Hyper Autonomous Driving Enhanced Edition
- AI Deployment in Intelligent Driving (6): Genesis World Model (1)
- AI Deployment in Intelligent Driving (6): Genesis World Model (2)
- AI Deployment in Intelligent Driving (7): Xiaomi XLA
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (2): AI Voice
- AI Deployment in Intelligent Cockpits (3): Connecting Alibaba Tongyi Large Model
- AI Deployment in Intelligent Cockpits (4): HyperOS Smart Cabin
- 5.5 Geely
- AI Strategy: Full-Domain AI (1)
- AI Strategy: Full-Domain AI (2)
- AI Data Strategy
- AI Computing Power Strategy (1): Cloud Computing Power
- AI Computing Power Strategy (2): Edge Computing Power
- AI Chip Strategy (1): Self-developed Chip Planning
- AI Chip Strategy (2): Self-developed Chip Longying No.1 (SE1000)
- AI Chip Strategy (3): Chip Cooperation
- AI Deployment Strategies for Operating Systems (1): Flyme AI OS
- AI Deployment Strategies for Operating Systems (2): ZEEKR AI OS
- AI Deployment Strategies for Operating Systems (3): SOA Atomic Service
- AI Deployment Strategies for Operating Systems (4): Upgrading AIOS Vehicle Intelligent Operating System
- AI Deployment in Intelligent Driving (1): AI Foundation Model Iteration
- AI Deployment in Intelligent Driving (2): E2E-based Haohan Intelligent Driving 2.0 Solution (1)
- AI Deployment in Intelligent Driving (2): E2E-based Haohan Intelligent Driving 2.0 Solution (2)
- AI Deployment in Intelligent Driving (3): Xingrui Large Model + DeepSeek-R1
- AI Deployment in Intelligent Driving (4): VLA+World Generation Model+AI Drive Large Model
- AI Deployment in Intelligent Driving (5): World Action Model
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (3): AI Agents
- AI Deployment in Intelligent Cockpits (4): Eva and AI Box (1)
- AI Deployment in Intelligent Cockpits (4): Eva and AI Box (2)
- AI Deployment in Intelligent Cockpits (5): Super Eva+G-ASD 4.0 (1)
- AI Deployment in Intelligent Cockpits (5): Super Eva+G-ASD 4.0 (2)
- AI Deployment in Intelligent Chassis (1): AI Digital Chassis
- AI Deployment in Intelligent Chassis (2): AI Power Control and Battery Management
- 5.6 BYD
- AI Strategy (1): Vehicle Intelligence Strategy
- AI Strategy (2): Three Strategic Goals
- AI Computing Power Strategy: Edge-Cloud Computing Power
- AI Chip Strategy (1): Self-developed 4nm Xuanji A3 Chip
- AI Chip Strategy (2): Chip Cooperation
- AI Chip Strategy (3): BYD9000 Jointly Developed with MediaTek
- AI Deployment Strategies for Operating Systems: BYD OS Architecture
- AI Deployment in Intelligent Driving (1): AI Foundation Model Iteration
- AI Deployment in Intelligent Driving (2): DiPilot (1)
- AI Deployment in Intelligent Driving (2): DiPilot (2)
- AI Deployment in Intelligent Driving (3): Xuanji AI Large Model (1)
- AI Deployment in Intelligent Driving (3): Xuanji AI Large Model (2)
- AI Deployment in Intelligent Driving (4): Integrating Deepseek R1 Large Model
- AI Deployment in Intelligent Cockpits (1): AI Application
- AI Deployment in Intelligent Cockpits (2): DiLink Cockpit Platform
- AI Deployment in Intelligent Cockpits (3): AI Multimodal Interaction
- AI Deployment in Intelligent Cockpits (4): Integrating Tongyi Large Model
- AI Deployment in Intelligent Cockpits (5): Integrating Doubao Large Model
- AI Deployment in Intelligent Cockpits (6): Synchronous Upgrades of Software and Hardware, Planned Application of AI-Powered Intelligent Cockpits
- 5.7 AITO
- AI Strategy
- AI Data Strategy (1): Data Collection
- AI Data Strategy (2): Data Training
- AI Computing Power Strategy (1): Edge-Cloud Collaboration
- AI Computing Power Strategy (2): Edge Computing Power
- AI Chip Strategy (1): Ascend AI chip
- AI Deployment Strategies for Operating Systems (1): HarmonyOS (1)
- AI Deployment Strategies for Operating Systems (1): HarmonyOS (2)
- AI Deployment Strategies for Operating Systems (2): Harmony Intelligent Mobility Alliance (HIMA)+Deepseek
- AI Deployment in Intelligent Driving (1): AI Foundation Model Iteration
- AI Deployment in Intelligent Driving (2): Layering of Pangu Model
- AI Deployment in Intelligent Driving (3): MoLA (1)
- AI Deployment in Intelligent Driving (3): MoLA (2)
- AI Deployment in Intelligent Driving (3): MoLA (3)
- AI Deployment in Intelligent Driving (4): Cloud World Engine of HIMA ADS 4
- AI Deployment in Intelligent Driving (5): Automotive World Action Model of HIMA ADS 4
- AI Deployment in Intelligent Cockpits (1): AI Cockpit Foundation Model
- AI Deployment in Intelligent Cockpits (2): Qianwu Model (1)
- AI Deployment in Intelligent Cockpits (2): Qianwu Model (2)
- AI Deployment in Intelligent Cockpits (3): Xiaoyi (1)
- AI Deployment in Intelligent Cockpits (3): Xiaoyi (2)
- 5.8 Changan Automobile
- AI Strategy: Beidou Dubhe Strategy (1)
- AI Strategy: Beidou Dubhe Strategy (2)
- AI Data Strategy
- AI Computing Power Strategy (1): Cloud Computing Power
- AI Computing Power Strategy (2): Edge Computing Power
- AI Chip Strategy: Chip Cooperation
- AI Deployment Strategies for Operating Systems (1): Tops OS
- AI Deployment Strategies for Operating Systems (2): Integrate AI into SOA Layer
- AI Deployment in Intelligent Driving: BEV+LLM+GoT E2E System
- AI Deployment in Intelligent Cockpits (1): StellarWave Model
- AI Deployment in Intelligent Cockpits (2): SDA Cockpit
- AI Deployment in Intelligent Cockpits (3): Topspace
- AI Deployment in Intelligent Cockpits (4): Deepal OS Intelligent Cockpit
- AI Deployment in Intelligent Cockpits (5): Deepal AI Intelligent Cockpit
- 5.9 BAIC
- AI Strategy: "Baimo Huichuang" Strategy
- AI Computing Power Strategy: Edge Computing Power
- AI Chip Strategy: Chip Cooperation
- AI Deployment Strategies for Operating Systems (1): AIOS
- AI Dep
