AI-Defined Vehicle (AIDV) OEMs' Deployment Strategies Research Report, 2026

Description

AIDV 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.

Product Code: ZXF017

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