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Chinese OEMs’ AI-Defined Vehicle Strategy Research Report, 2025
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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.

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

2 OEMs’ AI Infrastructure Layer Layout: Data + Computing Power  
2.1 AI-Defined Vehicle Infrastructure Layer: Data  
2.1.1 AI Applications in Vehicle Data Collection, Transmission, and Storage  
2.1.2 AI Applications in Vehicle Data Processing, Annotation, and Training  
2.1.3 Cases of AI Application in OEMs’ Data Closed-Loop (1)  
2.1.3 Cases of AI Application in OEMs’ Data Closed-Loop (2)   
2.1.4 Summary of OEMs’ AI Data Closed-Loop Capabilities  
2.1.5 Cases of AI Application in Suppliers’ Data Closed-Loop Products (1)  
2.1.5 Cases of AI Application in Suppliers’ Data Closed-Loop Products (2)  
2.1.5 Cases of AI Application in Suppliers’ Data Closed-Loop Products (3)  
2.1.6 Summary of AI Application in Suppliers’ Data Closed-Loop Products (1)  
2.1.6 Summary of AI Application in Suppliers’ Data Closed-Loop Products (2)   
2.1.7 Supported by AI Technology, the Ultimate Form of Data Closed-Loop May Be "Self-Evolving System"   
2.1.8 Suppliers’ AI Data Annotation Application Cases (1)  
2.1.8 Suppliers’ AI Data Annotation Application Cases (2)   
2.1.9 Summary of Suppliers’ AI Data Annotation Products (1)   
2.1.9 Summary of Suppliers’ AI Data Annotation Products (2)  

2.2 Data Is the Core Raw Material for AI Technology  
2.2.1 Data Has Evolved from an Auxiliary Resource to the Core Material for AI Foundation Models (1)  
2.2.1 Data Has Evolved from an Auxiliary Resource to the Core Material for AI Foundation Models (2)   
2.2.2 The Scale and Quality of Data Determine Model Performance  

2.3 AI-Defined Vehicle Infrastructure Layer: Cloud Computing Power  
2.3.1 Requirements for Cloud Computing Power in AI Technology Application and Solutions  
2.3.2 How OEMs Build Cloud Computing Power Required by AI (1)  
2.3.2 How OEMs Build Cloud Computing Power Required by AI (2)   
2.3.3 Cases of OEMs Collaborating with Third Parties to Build Cloud Computing Power Required by AI 
2.3.4 Summary of Chinese OEMs’ Cloud Computing Power Platforms (Partial)  

2.4 AI-Defined Vehicle Infrastructure Layer: Vehicle Computing Power  
2.4.1 Requirements for Vehicle Computing Power in AI Technology Applications and Solutions   
2.4.2 How OEMs Build Vehicle Computing Power Required by AI  
2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (1)    
2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (2)  
2.4.3 Cases of OEMs Developing In-House Vehicle AI Computing Chips (3)  
2.4.4 Summary of OEMs’ Self-developed Vehicle Computing Chips  

3 OEMs’ AI Model Layer Layout  
3.1 Overview of Application of AI Foundation Models in the Automotive Sector  
3.1.1 Definition and Characteristics of AI Foundation Models  
3.1.2 Classification of AI Foundation Models and Their Applications in the Automotive Sector  
3.1.3 Application of AI Foundation Models in Different Vehicle Layers (1)  
3.1.3 Application of AI Foundation Models in Different Vehicle Layers (2)   
3.1.4 Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (1)  
3.1.4 Cockpit-Driving Integration Central Computing Architecture Provides A Favorable Environment for Implementation of AI-Defined Vehicles (2)  

3.2 Requirements of AI Foundation Models in Vehicle Chips   
3.2.1 Deployment of AI Foundation Models on the Terminal Will Continue to Drive Exponential Growth in Vehicle Chip Computing Power Demand   
3.2.2 Deployment of AI Foundation Models on the Terminal Calls for High-Compute, Low-Power Compute-in-Memory Chips   
3.2.3 Distillation and Compression of AI Foundation Models Can Lower Vehicle Computing Power Requirements  
3.2.4 Application Cases of Distillation and Compression of AI Foundation Models
3.2.5 Summary of Vehicle Chips Capable of Running AI Foundation Models  

3.3 Applications of AI Foundation Models in Vehicle Operating Systems  
3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (1)  
3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (2)  
3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (3)  
3.3.1 Impacts of AI Foundation Models on Vehicle Operating Systems (4)  
3.3.2 Cases of AI Large Model Application in Operating Systems (1)  
3.3.2 Cases of AI Large Model Application in Operating Systems (2)  
3.3.2 Cases of AI Large Model Application in Operating Systems (3)   
3.3.2 Cases of AI Large Model Application in Operating Systems (4)  
3.3.3 AI Foundation Models Can Be Used to Generate Autosar Tests  

3.4 Application of AI Foundation Models in Intelligent Driving  
3.4.1 Application of AI Foundation Models in Intelligent Driving (1)  
3.4.1 Application of AI Foundation Models in Intelligent Driving (2)  
3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (1)   
3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (2)  
3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (3)  
3.4.2 Generative Simulation Technology for AI Foundation Models Improves Realism of Driving Simulation Systems and Enriches Simulation Scenarios (4)  
3.4.3 Application Cases of Generative Simulation Technology for AI Foundation Models (1)   
3.4.3 Application Cases of Generative Simulation Technology for AI Foundation Models (2)   
3.4.4 Application of AI Foundation Models in Intelligent Driving Perception (1)  
3.4.4 Application of AI Foundation Models in Intelligent Driving Perception (2)  
3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (1)   
3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (2)  
3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (3)  
3.4.5 Cases of Application of AI Foundation Models in Intelligent Driving Perception by OEMs (4)  
3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (1)  
3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (2)  
3.4.6 Cases of Application of AI Foundation Models in Intelligent Driving Perception by Suppliers (3)   
3.4.7 Application of AI Foundation Models in Intelligent Driving Decision (1)  
3.4.7 Application of AI Foundation Models in Intelligent Driving Decision (2)  
3.4.8 Cases of Application of AI Foundation Models in Intelligent Driving Decision by OEMs (1)   
3.4.8 Cases of Application of AI Foundation Models in Intelligent Driving Decision by OEMs (2)  
3.4.9 Cases of Application of AI Foundation Models in Intelligent Driving Decision by Suppliers (1)  
3.4.9 Cases of Application of AI Foundation Models in Intelligent Driving Decision by Suppliers (2)   
3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (1)  
3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (2)  
3.4.10 Trends in Application of AI Foundation Models in Intelligent Driving (3)  

3.5 Application of AI Foundation Models in Intelligent Cockpit and Interaction  
3.5.1 Application of AI Foundation Models in Intelligent Cockpit: AI-Defined Cockpit vs. Software-Defined Cockpit    
3.5.2 Application Scenarios of AI Foundation Models in Intelligent Cockpit  
3.5.3 Application of AI Foundation Models in Intelligent Cockpit Interaction Design: Enabling Emotional Interaction (1)   
3.5.3 Application of AI Foundation Models in Intelligent Cockpit Interaction Design: Enabling Emotional Interaction (2)   
3.5.4 Application of AI Foundation Models in Intelligent Cockpit HUD 
3.5.5 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (1)   
3.5.5 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction (2)   
3.5.6 Application of AI Foundation Models in Intelligent Cockpit Voice Interaction: Summary of Supplier Solutions  
3.5.7 Application of AI Foundation Models in Intelligent Cockpit Gesture Recognition   
3.5.8 Application of AI Foundation Models in Intelligent Cockpit Monitoring  
3.5.9 AI Algorithms Used by AI Foundation Models in Intelligent Cockpit Monitoring 
3.5.10 Cases of Application of AI Foundation Models in Intelligent Cockpit Monitoring (1)   
3.5.10 Cases of Application of AI Foundation Models in Intelligent Cockpit Monitoring (2)   
3.5.11 Application of AI Foundation Models in Intelligent Cockpit Personalized Services   
3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (1)  
3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (2)  
3.5.12 Trends in Application of AI Foundation Models in Intelligent Cockpit (3)  

3.6 Summary of OEMs’ AI Foundation Model Applications   
3.7 Summary of Suppliers’ AI Foundation Model Applications  
3.8 Summary of Mainstream AI Foundation Models in China 
3.9 Challenges in Application of AI Foundation Models in the Automotive Sector and Development Trends
3.9.1 Challenges in Application of AI Foundation Models in the Automotive Sector and Solutions (1)  
3.9.1 Challenges in Application of AI Foundation Models in the Automotive Sector and Solutions (2)  
3.9.2 Trend 1 in Application of AI Foundation Models in the Automotive Sector  
3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (1)  
3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (2)  
3.9.3 Trend 2 in Application of AI Foundation Models in the Automotive Sector (3)  
3.9.4 Trend 3 in Application of AI Foundation Models in the Automotive Sector  
3.9.5 Trend 4 in Application of AI Foundation Models in the Automotive Sector

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

5 OEMs’ Progress and Layout in AI-Defined Vehicles
5.1 Li Auto
5.1.1 AI Layout
5.1.1 Strategy for AI (1)
5.1.1 Strategy for AI (2)
5.1.1 Strategy for AI (3)
5.1.2 AI R&D Investment and Team Building
5.1.3 AI Data Strategy (1)
5.1.3 AI Data Strategy (2)
5.1.3 AI Data Strategy (3)
5.1.3 AI Data Strategy (4)
5.1.4 AI Compute Layout (1)
5.1.4 AI Compute Layout (2
5.1.4 AI Compute Layout (3)
5.1.4 AI Compute Layout (4)
5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (1)
5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (2)
........................
5.1.5 Autonomous Driving VLA Solution Based on End-to-end and VLM (7)
5.1.6 Vehicle Operating System for AI (1)
5.1.6 Vehicle Operating System for AI (2)
5.1.7 Underlying Algorithms for End-to-end Autonomous Driving Solutions (1)
........................
5.1.7 Underlying Algorithms for End-to-end Autonomous Driving Solutions (5)
5.1.8 AI Foundation Model Training Platform: Using 4D Parallel Approach
5.1.9 AI Agent (1)
5.1.9 AI Agent (2)
........................
5.1.9 AI Agent (8)
5.1.9 AI Agent (9)
5.1.10 AI Multimodal Interaction Application Scenarios (1)
5.1.10 AI Multimodal Interaction Application Scenarios (2)
........................
5.1.10 AI Multimodal Interaction Application Scenarios (6)
5.1.11 AI Application in R&D and Production (1)
5.1.11 AI Application in R&D and Production (2)


5.2 NIO
5.2.1 AI Layout
5.2.1 Strategy for AI (1)
5.2.1 Strategy for AI (2)
5.2.1 Strategy for AI (3)
5.2.2 AI Compute Layout (1)
........................
5.2.2 AI Compute Layout (5)
5.2.3 Vehicle Operating System for AI (1)
5.2.3 Vehicle Operating System for AI (2)
........................
5.2.3 Vehicle Operating System for AI (7)
5.2.4 AI-based Autonomous Driving Solutions (1)
........................
5.2.4 AI-based Autonomous Driving Solutions (7)
5.2.5 AI Application in Intelligent Cockpit (1)
5.2.5 AI Application in Intelligent Cockpit (2)
........................
5.2.5 AI Application in Intelligent Cockpit (11)
5.2.5 AI Application in Intelligent Cockpit (12)

5.3 Xpeng 
5.3.1 AI Layout
5.3.1 Strategy for AI (1)
5.3.1 Strategy for AI (2)
5.3.1 Strategy for AI (3)
5.3.1 Strategy for AI (4)
5.3.2 AI Data Strategy (1)
5.3.2 AI Data Strategy (2)
5.3.2 AI Data Strategy (3)
5.3.3 AI Compute Layout (1)
5.3.3 AI Compute Layout (2)
........................
5.3.3 AI Compute Layout (8)
5.3.4 Vehicle Operating System for AI (1)
5.3.4 Vehicle Operating System for AI (2)
5.3.4 Vehicle Operating System for AI (3)
5.3.4 Vehicle Operating System for AI (4)
5.3.5 AI-based End-to-end Autonomous Driving Solution (1)
........................
5.3.5 AI-based End-to-end Autonomous Driving Solution (6)
5.3.5 AI-based End-to-end Autonomous Driving Solution (7)
5.3.6 AI Application in Intelligent Cockpit (1)
5.3.6 AI Application in Intelligent Cockpit (2)
5.3.6 AI Application in Intelligent Cockpit (3)
5.3.6 AI Application in Intelligent Cockpit (4)
5.3.6 AI Application in Intelligent Cockpit (5)

5.4 Xiaomi Auto
5.4.1 AI Strategy
5.4.2 AI Data Strategy
5.4.3 AI Compute Layout
5.4.4 Vehicle Operating System for AI (1)
........................
5.4.4 Vehicle Operating System for AI (7)
5.4.4 Vehicle Operating System for AI (8)
5.4.5 AI-based Autonomous Driving Solutions (1)
5.4.5 AI-based Autonomous Driving Solutions (2)
5.4.5 AI-based Autonomous Driving Solutions (3)
5.4.5 AI-based Autonomous Driving Solutions (4)
5.4.6 AI Cockpit (1)
........................
5.4.6 AI Cockpit (6)

5.5 Geely
5.5.1 AI Layout
5.5.1 Strategy for AI (1)
5.5.1 Strategy for AI (2)
5.5.1 Strategy for AI (3)
5.5.1 Strategy for AI (4)
5.5.1 Strategy for AI (5)
5.5.2 AI Data Strategy (1)
5.5.2 AI Data Strategy (2)
........................
5.5.2 AI Data Strategy (7)
5.5.3 AI Compute Layout (1)
5.5.3 AI Compute Layout (2)
5.5.3 AI Compute Layout (3)
5.5.2 AI Data Strategy (4)
5.5.4 Vehicle Operating System for AI (1)
........................
5.5.4 Vehicle Operating System for AI (6)
5.5.5 AI-based Autonomous Driving Solutions (1)
5.5.5 AI-based Autonomous Driving Solutions (2)
........................
5.5.5 AI-based Autonomous Driving Solutions (6)
5.5.6 AI Application in Intelligent Cockpit (1)
5.5.6 AI Application in Intelligent Cockpit (2)
5.5.6 AI Application in Intelligent Cockpit (3)
5.5.6 AI Application in Intelligent Cockpit (4)
5.5.7 AI Chassis (1)
5.5.7 AI Chassis (2)
5.5.8 AI Application Cases in Production, Sales and Service
5.5.9 Xingrui Agent Platform for Production 

5.6 BYD
5.6.1 AI Layout
5.6.1 Strategy for AI (1)
5.6.1 Strategy for AI (2)
5.6.1 Strategy for AI (3)
5.6.2 AI Data Strategy (1)
5.6.2 AI Data Strategy (2)
5.6.2 AI Data Strategy (3)
5.6.3 AI Compute Layout
5.6.4 AI-based Vehicle Intelligent Architecture: Xuanji Architecture
5.6.5 AI-based Autonomous Driving Solutions (1)
5.6.5 AI-based Autonomous Driving Solutions (2)
5.6.5 AI-based Autonomous Driving Solutions (3)
5.6.5 AI-based Autonomous Driving Solutions (4)
5.6.6 AI Application in Intelligent Cockpit (1)
5.6.6 AI Application in Intelligent Cockpit (2)
5.6.7 AI-powered Manufacturing

5.7 Changan
5.7.1 Digital Strategy (1)
........................
5.7.1 Digital Strategy (6)
5.7.2 AI-based Vehicle Operating System
5.7.3 AI-based Autonomous Driving Solutions (1)
5.7.3 AI-based Autonomous Driving Solutions (2)
5.7.3 AI-based Autonomous Driving Solutions (3)
5.7.4 AI Application in Intelligent Cockpit (1)
........................
5.7.4 AI Application in Intelligent Cockpit (5)
5.7.5 AI-powered Manufacturing (1)
5.7.5 AI-powered Manufacturing (2)

5.8 BAIC
5.8.1 Intelligent Cockpit AI Agent (1)
5.8.1 Intelligent Cockpit AI Agent (2)
5.8.1 Intelligent Cockpit AI Agent (3)
5.8.2 AI-based Vehicle Operating System
5.8.3 AI Application in Intelligent Cockpit (1)
........................
5.8.3 AI Application in Intelligent Cockpit (7)
5.8.3 AI Application in Intelligent Cockpit (8)

5.9 Great Wall Motor 
5.9.1 Strategy for AI
5.9.2 AI Data Strategy (1)
5.9.2 AI Data Strategy (2)
5.9.2 AI Data Strategy (3)
5.9.3 AI Compute Layout (1)
5.9.3 AI Compute Layout (2)
5.9.3 AI Compute Layout (3)
5.9.3 AI Compute Layout (4)
5.9.4 AI-based Vehicle Operating System
5.9.5 AI-based Autonomous Driving Solutions (1)
5.9.5 AI-based Autonomous Driving Solutions (2)
5.9.5 AI-based Autonomous Driving Solutions (3)
5.9.6 AI Application in Intelligent Cockpit (1)
5.9.6 AI Application in Intelligent Cockpit (2)

5.10 Chery
5.10.1 Strategy for AI (1)
5.10.1 Strategy for AI (2)
5.10.1 Strategy for AI (3)
5.10.2 AI Data Strategy
5.10.3 AI-based Autonomous Driving Solutions (1)
5.10.3 AI-based Autonomous Driving Solutions (2)
5.10.3 AI-based Autonomous Driving Solutions (3)
5.10.3 AI-based Autonomous Driving Solutions (4)
5.10.4 AI Application in Intelligent Cockpit (1)
5.10.4 AI Application in Intelligent Cockpit (2)
5.10.4 AI Application in Intelligent Cockpit (3)

5.11 SAIC
5.11.1 Strategy for AI (1)
5.11.1 Strategy for AI (2)
5.11.1 Strategy for AI (3)
5.11.1 Strategy for AI (4)
5.11.2 AI Data Strategy (1)
5.11.2 AI Data Strategy (2)
5.11.2 AI Data Strategy (3)
5.11.2 AI Data Strategy (4)
5.11.3 Vehicle Operating System for AI (1)
5.11.3 Vehicle Operating System for AI (2)
........................
5.11.4 AI-based Autonomous Driving Solutions (1)
5.11.4 AI-based Autonomous Driving Solutions (2)
........................
5.11.5 AI Application in Intelligent Cockpit (1)
5.11.5 AI Application in Intelligent Cockpit (2)
........................

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