Automotive AIOS Research Report, 2026

Description

Automotive AIOS Research: Mass Production Solutions Are Implemented

Mass Production Solutions Are Implemented on A Small Scale.

In 2026, AIOS starts small-scale implementation, helping to improve various cockpit AI functions and enable more comprehensive application scenarios. In addition, the AIOS of some mainstream flagship vehicle models realizes cross-domain orchestration capabilities through atomic services, expanding execution capabilities to body, chassis, intelligent driving and other domains.

As of June 2026, OEMs have still adopted two AIOS R&D models: self-development and semi-outsourcing:

Full-stack self-development: Emerging automakers led by NIO and Li Auto deeply integrate AI capabilities into the middleware layer (even kernel layer), forming a full-stack closed loop from chip to application.

Semi-outsourcing: Traditional OEMs independently build the vertical large model + AIOS framework, reusing basic software from suppliers at the bottom layer.

The in-vehicle deployment modes of mass-produced AIOS solutions of suppliers include the following:

Extending from cockpit AI applications to the bottom layer of OS: the mainstream approach. For example, Huawei provides HarmonyOS-related services via HarmonySpace.

Binding with chip/hardware manufacturers: Adapt to chip solutions of multiple chip manufacturers for software-hardware collaboration. Typical examples include Sage Box for SenseTime SageOS and AI Box-N1 for ThunderSoft AquaDrive OS, which build comprehensive on-device AI solutions coordinated with AI Box.

Binding with cloud providers: Represented by Extour Technology, bind with Volcano Engine's cloud base and invokes Doubao Large Model to provide AI services.

Compared with 2025, cross-domain invocation services of OS became more mature in 2026.

In the case of Dongfeng Tianyuan OS, the entire architecture realizes integration across five domains: body, powertrain, chassis, thermal management and gateway. Based on the Taichi Large Model base, it invokes more than 2,000 atomic services and supports rapid combination and flexible invocation of functions via service-oriented architecture.

In the process of AIOS deployment, the software foundation for cross-domain invocation of cockpit software system is still the vehicle OS. Based on the vehicle OS, non-safety cockpit functions can be disassembled atomically. Then, automotive intelligent scheduling algorithms carried by AI middleware realize dynamic allocation and intelligent scheduling of computing power, applications and peripheral resources in the cockpit. When the user issues an instruction, the voice assistant disassembles intentions, coordinates multiple AI frameworks to work, and finally invokes atomic services. This entire process is the basic workflow of the AIOS.

In 2026, the number of interfaces for atomic capabilities surged (mainstream flagship vehicle models generally have more than 500 atomic capabilities). Against the backdrop of increasingly popular customized cockpit scenarios, the competitive edges of AIOS have gradually shifted from "more atomic capabilities" to "easier combination of atomic capabilities". Protocols for customized and standardized interfaces are critical on the issue of "whether to combine more easily".

Some of the engineering challenges involved in highlighting the effects of AIOS are as follows:

The lack of standardized unified protocols makes it very easy to hinder the effectiveness of atomic capabilities under SOA. At present, Function Call is the mainstream protocol adopted, while MCP is still in the trial stage. The reason is that Function Call can meet core requirements and is easy to maintenance under small-scale mass production conditions. However, when large-scale migration of solutions is required, wrapping MCP Server outside Function Call demonstrates advantages of "cross-model portability" and "dynamic tool discovery".

The significance of the MCP protocol lies in standardization, compressing the development cycle of cross-scenario functions from months to weeks. Automakers can quickly combine personalized cockpit services like building blocks. Typical cases include Extour Technology's automotive MCP-Agent framework and SenseAuto's edge native agent framework supporting MCP/A2A protocols.

For example, SenseAuto launched an edge native agent framework supporting MCP/A2A protocols. It builds a standardized "Agent-Tool" integration framework, allowing multiple agents to efficiently integrate various vehicle tools such as players, air conditioners and knowledge bases through a unified MCP protocol layer, solving difficulties in tool invocation, data acquisition and multi-source information integration during agent development.

Its advantages include:

Cost reduction and efficiency improvement: The unified protocol eliminates fragmentation barriers for tool docking, greatly cutting development and collaboration costs and enabling all types of tools to be "plug-and-play".

Open ecosystem: Supports a standardized ecosystem access mechanism, facilitating rapid integration of third-party services and hardware into intelligent vehicle systems, and promoting diversified ecosystems.

Controllable security: Unified security authentication policies and centralized management simplify processes while strengthening system security.

Next Stage: Shift from AI-Driven to AI-Native

Suppliers including Huawei and Neusoft divide the integration of AI and OS into three stages:

In 2025, most OEMs and suppliers built AI operating systems by deploying the AI framework at the middleware layer. Examples include XPeng's deployment of cross-domain unified protocol middleware + on-device large model + atomic services, Great Wall Motor's deployment of multi-model base and Agent management/operation framework at the middleware layer, and Neusoft Reach's deployment of NeuSAR AI Framework on NeuSAR OS for rapid introduction of AI applications into vehicles.

In 2026, leading emerging OEMs and suppliers start deploying "AI-enhanced kernels" to build kernel-layer native AIOS. For instance, NIO leverages AI to improve the OS kernel's ability to dynamically schedule resources according to scenarios; Huawei HarmonyOS kernel natively supports multi-modal understanding and personalized data understanding.

In addition, with the deployment of agent technology and upgrading of high-compute chips, the AIOS architecture has also undergone changes from the application layer to the bottom layer.

For example, NIO's new SkyOS deeply integrates AI capabilities into the bottom layer of the operating system, replacing the traditional architectural paradigm. It realizes efficient end-cloud integrated collaboration, intelligent scheduling of heterogeneous computing power, and multi-agent collaboration, while improving system response speed, stability, data throughput and battery life. Its innovation lies in enhancing the kernel's ability to dynamically schedule resources according to scenarios, including CPU (process and thread priority), memory management (allocation and recycling) and device sharing. It can boost system stability and response speed in high-load scenarios.

In the case of Huawei Qiankun OS, it contains a security isolation engine, AI-native kernel, UnifiedBus, acceleration engine and Bisheng Compiler, providing deterministic low-latency rapid response for upper-layer ADS algorithms. Its kernel is based on HarmonyOS kernel but deeply tailored and reconstructed for automotive scenarios to become an AI-native kernel, and can realize seamless flow of "vehicle-road-cloud-mobile phone".

Product Code: GX024

1 Status Quo and Development Trends of Automotive AIOS

1.1 Status Quo of AIOS
From Vehicle OS to Vehicle AIOS (1)
From Vehicle OS to Vehicle AIOS (2)
From Vehicle OS to Vehicle AIOS (3)
Relationship between Vehicle OS and AIOS
Overview of AI Application in Automotive OS (1)
Overview of AI Application in Automotive OS (2)
Overview of AI Application in Automotive OS (3)
AIOS Layout of Suppliers (1)
AIOS Layout of Suppliers (2)
AIOS Layout of OEMs (1)
AIOS Layout of OEMs (2)
1.2 AIOS Architecture and Technical Analysis
AIOS Architecture (1): Deployment Structure
AIOS Architecture (1): Functional Characteristics of Different Layers
AIOS Architecture (1): AI Toolchain
AIOS Technologies (2): Technical Features in Deployment
AIOS Technologies (2): Technical Framework and Functions
AIOS Technologies (2): Vehicle Abstraction
AIOS Technologies (2): Vehicle Base Abstraction
AIOS Technologies (2): Multi-Modal Fusion
AIOS Technologies (2): Security Mechanisms
AIOS Technologies (2): Challenges and Solutions
AIOS Technologies (2): Challenges and Countermeasures
1.3 Development Trends of AIOS
Trend 1: AIOS Enters Small-Scale Mass Production Stage
Trend 2:
Trend 3:
Trend 4:
Trend 5: AIOS Moves from AI-Driven Stage to AI-Native Stage
Trend 5: Case 1
Trend 5: Case 2
Outlook (1): Required Characteristics of AI-Native OS
Outlook (2): AIOS Generated by Hybrid Kernel Solutions
1.4 Technical Analysis of Cutting-Edge AIOS
Construction Methods of LLM OS
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 (17)
AIOS Architecture: Throughput and Latency of AIOS under Parallel Operation
AIOS Architecture: Performance Maintenance of AIOS under Parallel Operation
AIOS Architecture: Model Deployment and Task Workflow of AIOS
AIOS Architecture: Comparison between Different AI Runtimes
AIOS-derived Framework: LSFS Functions (1)
AIOS-derived Framework: LSFS Functions (2)
AIOS-derived Framework: LSFS Functions (6)

2 Vehicle OS and Basic Operating Systems

2.1 Definitions and Development History
Automotive Operating Systems
Development History of Operating Systems
Vehicle OS: Definition
Software Layer Architecture
Characteristics of Vehicle OS
Evolution of Vehicle OS Development Models: By Automotive Architecture
Evolution of Vehicle OS Development Models: By R&D Model
Evolution of Vehicle OS Business Models
Summary of OEMs' Vehicle OS (1)
Summary of OEMs' Vehicle OS (2)
Summary of OEMs' Vehicle OS (8)
Vehicle OS Cross-domain Invocation: Algorithm Invocation
2.2 Development Trends of Automotive Operating Systems
Trend 1:
Trend 2: Impacts of Open-Source Ecosystem on Competitive Landscape
Trend 2: Impacts of Open-Source Ecosystem on Software Business Models
Trend 3: OEMs' Operating System Layout Modes
Trend 3: Suppliers' Vehicle OS Layout Modes (1)
Trend 3: Suppliers' Vehicle OS Layout Modes (2)
Trend 4: OEMs' Self-Developed Vehicle OS - Advantages and Disadvantages
Trend 4: OEMs' Self-Developed Vehicle OS - Decision-Making Process
Trend 4: OEMs' Self-Developed Vehicle OS - Gradient Status
Trend 4: OEMs' Self-Developed Vehicle OS - Upper-Layer Application Ecosystem
Trend 4: OEMs' Self-Developed Vehicle OS - Middleware
Trend 4: OEMs' Self-Developed Vehicle OS - Communication Middleware
Trend 4: OEMs' Self-Developed Vehicle OS - Cost Control
Core Competitive Factors of OEMs' Self-Developed Vehicle OS
2.3 Classification of Automotive Operating Systems
Classification of Automotive Operating Systems: Narrow-Sense and Broad-Sense Automotive OS
Classification of Automotive Operating Systems: Real-Time and Non-Real-Time Automotive OS
List of RTOS Suppliers and Products (1)
List of RTOS Suppliers and Products (2)
List of RTOS Suppliers and Products (3)
List of Non-RTOS Suppliers and Products (1)
List of Non-RTOS Suppliers and Products (2)
Classification of Automotive Operating Systems: Microkernel, Macro Kernel, Hybrid Kernel
Classification of Automotive Operating Systems: Vehicle Control OS and Vehicle OS
Automotive Operating System Market Size Forecast
2.4 Software Architecture
Typical Broad-Sense OS Architecture
Intelligent Vehicle Software Ecosystem Framework
Kernel Is the Core of Automotive Software Architecture
2.5 Business Models
Types of Automotive Operating System Business Models
Business Models of Major Automotive OS Companies
Development Trends and Business Model Exploration of Automotive Operating Systems
Basic Automotive Operating System and Business Models
Automotive RTOS and Business Models (1)
Automotive RTOS and Business Models (2)
Operating System Business Models of Suppliers (1)
Operating System Business Models of Suppliers (2)
Operating System Business Models of Suppliers (3)
Operating System Business Models of Suppliers (4)
2.6 Automotive Electronic Standards: AUTOSAR
Profile of AUTOSAR
AUTOSAR Classification
Core Members
Classic AUTOSAR: Architecture
Classic AUTOSAR: Functions
Adaptive AUTOSAR: Framework
Comparison between Classic AUTOSAR and Adaptive AUTOSAR
Integrated Application of Adaptive AUTOSAR and ROS
Highlights of AUTOSAR
Architecture of AUTOSAR China Working Group
AUTOSAR China Working Group Project Cases
Business Models of AUTOSAR-Related Software Tool Suppliers (1)
Business Models of AUTOSAR-Related Software Tool Suppliers (2)
Business Models of AUTOSAR-Related Software Tool Suppliers (7)
Vector's AUTOSAR Solution Business Model
EB's AUTOSAR Solution Business Model
Neusoft Reach's AUTOSAR Solution Business Model
iSOFT Infrastructure Software's AUTOSAR Solution Business Model
Jingwei Hirain's AUTOSAR Solution Business Model
2.7 Middleware Layout
Comparison of Core OS Middleware Components between Major OEMs: XPeng, NIO, Li Auto, etc.
Comparison of Core OS Middleware Components between Major Suppliers: Huawei, ThunderSoft, ArcherMind, etc.
2.8 BlackBerry
Development History of QNX in Automotive Field
QNX Business
QNX Products: Safety Levels
QNX Products: Features of RTOS
QNX Products: RTOS Architecture
QNX Products: Cockpit Software Platform Solution (SDP8.0)
QNX Products: ADAS Platform
QNX Products: Cockpit-Driving Integrated Controller
QNX Products: QNX Cloud Simulation Platform
QNX Products: Domain Controller Basic Software Platform
QNX OS for Safety: Product Overview
QNX OS for Safety: Safety Performance Comparison
QNX Application in Robotics
QNX Partners
Latest Dynamics of QNX
2.9 Linux & AGL
AGL Members
Linux Architecture
RT-Linux
Linux Foundation AI Open-Source Projects
AGL Application Framework: UCB
2.10 Android
Introduction to Android & Android Automotive OS
Android Automotive OS Architecture (1)
Android Automotive OS Architecture (2)
Features of Android Automotive OS
AI Functions Introduced to Android Auto
Impacts of Slowing AOSP Update Pace
User Development Status
2.11 Huawei
Introduction to HarmonyOS
Development History of HarmonyOS
Technical Architecture of HarmonyOS
HarmonyOS Technical Architecture: Future Development Directions
Cooperation Models between HarmonyOS and Automakers
Intelligent Driving OS - AOS
Intelligent Vehicle Control OS - VOS
Cross-Domain Integrated Software Framework Vehicle Stack
Upgrade of iDVP Platform
Qiankun OS Meeting Requirements of AI+SOA Enabled Autonomous Vehicles
Qiankun OS Integrates World Model
CCA: VCU (Central Computing) + 3-5 VIUs (ZCUs)
CCA: System Framework and Full-Stack Solution
AI Functions of HarmonyOS
Two Implementation Modes of "See and Speak" on HarmonyOS
2.12 Alibaba
Introduction to AliOS
Banma Zhixing's Vehicle OS Evolution Strategy
AliOS Operating System Architecture
AliOS Application Layer
Integration of Alibaba Qwen Large Model and OS: System Agent System
Integration of Alibaba Qwen Large Model and OS: Yan AI Series
AliOS Solution: AliOS Intelligent Cockpit OS
AliOS Drive Intelligent Driving OS
Banma Zhixing's OS Business Model
Banma Hypervisor Facilitates Upgrade
Latest Dynamics of AliOS
2.13 VxWorks
Introduction to VxWorks
Wind River VxWorks Microkernel Architecture (1)
Wind River VxWorks Microkernel Architecture (2)
WindRiver Products: WindRiver Linux and WindRiver AUTOSAR Adaptive Software Platform
WindRiver Products: Helix Virtualization Platform
New RTOS Products of WindRiver
Latest Dynamics in Automotive Field
2.14 Ubuntu
Profile
Application
Cooperation in Automotive Field
2.15 webOS
Development History
webOS OSE Components and Development Roadmap
Integration of webOS and AGL
Latest Dynamics in Automotive Field
2.16 ROS
Introduction to ROS
Introduction to ROS 2.0
Iteration History of ROS 2.0
Differences between ROS 2 and Other Middleware
ROS 2.0 Architecture
ROS Application Cases

3 AIOS Suppliers

3.1 Neusoft Reach
Evolution of NeuSAR
Introduction to NeuSAR
Three Stages of AIOS
AI Deployment of Vehicle Intelligent OS
Four Layers of NeuSAR OS Architecture
NeuSAR SF (Service Framework) Middleware
NeuSAR AI Framework Middleware Products
NeuSAR Copilot Facilitates Efficient AUTOSAR Development
NeuSAR OS Completed Adaptation to DeepSeek
NeuSAR aCore
Upgrade of AUTOSAR AP Products
NeuSAR cCore
Lightweight AUTOSAR CP Products
NeuSAR OS Cooperation: Infineon
NeuSAR OS Cooperation: NXP
Intelligent Driving OS
3.2 ThunderSoft
AquaDrive OS Vehicle OS
Integration of Rubik Foundation Model and Operating System
AquaDrive AIOS Application Architecture
AquaDrive AIOS Deployment Architecture
How AquaDrive OS Supports Implementation of AI Scenarios
Upgrade of AquaDrive AI OS: Addition of AquaClaw Automotive Agent
3.3 ArcherMind Technology
Development History of AIOS Products
Arraymo AIOS Base
Cross-Domain Vehicle OS: FusionOS 1.0
Cross-Domain Vehicle OS: FusionOS 2.0
Vehicle OS: FusionOS 4.0 AIOS
Firefly AIOS
Products Supported by AIOS
3.4 SenseTime
SenseAuto Qianji AIOS Kernel
Edge AI Solution Based on Edge Model and AIOS
3.5 Kotei Informatics
Evolution of AIOS and Middleware Product Layout
A2OS System
Three Versions of A2OS
3.6 Kernelsoft
AI-Oriented OS Solution
Real-Time Operating System
Linux
Operating System Security
3.7 SYNCORE AUTOTECH
Mass Production of AIOS
Upgrade of Overseas Version of OS
Cockpit AI OS Solution Enables "Proactivity" and "Symbiosis"
Underlying Operation Mechanism of AIOS
3.8 Extour Technology
Cockpit AI Solution Based on AIOS
MCP-Agent Framework Buids System Expansion Base for AIOS
Underlying Architecture of Xinjie AI System
Typical Functions of AI System
3.9 STEP
Upgrade of AI-Native Cross-Domain Fusion Operating System
AI-Native OS: Accelerate Intelligent Driving Project Development
AI-Native OS: Advantages of Middleware
AI-Native OS: Empower Humanoid Robots
Infrastructure LLMOS Serves Cockpit AI Applications
3.10 Others
CARThunder OS Based on Agentic AI Architecture
Hangsheng Electronics' AI OS Solution
Rockchip Launches A Series of Domestic Hardware-Software Integrated Intelligent Foundation Products Featuring AIOS
Horizon Robotics: Agentic Car OS Covers Cockpit-Driving Integration Scenarios
Megatronix's Vehicle Distributed Intelligent Operating System

4 Vehicle OS Suppliers

4.1 iSOFT Infrastructure Software
Software System Layout
Vehicle OS Layout: System Framework
Vehicle OS Layout: Development History
Vehicle Control OS: Architecture
Vehicle Control OS: Functions
Vehicle Control OS: Chip Ecosystem
Vehicle Control OS: Upgrade Cooperation with Chip Vendors
Vehicle Control OS: New-Generation Vehicle Control OS Platform Solution
Intelligent Driving OS: Features
Intelligent Driving OS: Security
Intelligent Driving OS: Architecture
AUTOSAR Solution
AUTOSAR CP+AP Integrated Solution
CP Products
4.2 ZTE GoldenOS
ZTE Builds New Software-Hardware Collaboration Paradigm of Chip + OS + AI
AI Promotes Domain Integration
Microkernel and Macro Kernel Technical Architectures
Vehicle Control OS Solution
Intelligent Cockpit OS Solution
Intelligent Driving OS Solution: Dual-Kernel Architecture
Intelligent Driving OS Solution: Application Scenarios
Intelligent Driving OS Solution: Evolution History
Intelligent Driving OS Solution: Chip Adaptation
Dynamics in Neusoft Reach + ZTE + SemiDrive Cooperation
4.3 Automotive Intelligence and Control of China Co., Ltd. (AICC)
Product System
ICVOS: Intelligent Connected Vehicle Operating System
ICVOS: Software Architecture
ICVOS: Development Architecture
ICVOS: SDK Architecture
ICVOS: Platformized, Networked and Scalable
ICVOS: Vehicle-Cloud Collaboration
ICVOS: Information Security Basic Platform
ICVOS: New Architecture Oriented to Autonomous Driving Domain
ICVOS: Cases of Software Architecture Co-development with OEMs (1)
ICVOS: Cases of Software Architecture Co-development with OEMs (2)
ICVOS: Cases of Software Architecture Co-development with OEMs (3)
ICVOS: Cases of Software Architecture Co-development with OEMs (4)
4.4 NVIDIA DRIVE OS
Introduction to Drive OS
Drive OS SDK Architecture
4.5 Elektrobit (EB)
Tresos Real-Time Operating System
Tresos AutoCore Architecture
Intelligent Driving Domain Operating System Based on J5
Virtualization Development Technology
4.6 Others
iHUATEK Uses Large Vision Model to Build Vehicle OS
Freetech SOA Integrates Large Models
Zlingsmart's "RAITE OS" Microkernel Operating System
Clarence Vehicle Integrated Software Platform (RTOS)
Red Hat

5 Operating Systems of Chinse OEMs

5.1 Li Auto
Vehicle OS: Evolution History
Vehicle OS: Architecture
SOA and Basic Software: Main Components of Vehicle OS HaloOS
Vehicle OS: Architecture
Vehicle OS: Components and Features
Vehicle OS: Components (1) - Communication Middleware
Vehicle OS: Components (1) - Features of Communication Middleware
Vehicle OS: Components (2) - Vehicle Control OS
Vehicle OS: Components (2) - Features of Vehicle Control OS
Vehicle OS: Components (3) - Intelligent Driving OS
Vehicle OS: Components (3) - Intelligent Driving OS Subsystems
Vehicle OS: Components (4) - Virtualization Engine
Vehicle OS: Components (4) - Features of Virtualization Engine
Vehicle OS: Components (5) - Information Security
Vehicle OS: Components (5) - Information Security Features
Vehicle OS: Components (5) - Information Security Scenarios
Vehicle OS: Innovative Scenario - Cross-Domain Sensor Sharing
HaloOS Application Advantage 1: Rich Adaptable Chip Types
HaloOS Application Advantage 2: Cross-Domain Scheduling
HaloOS Application Advantage 3: Hardware Sharing
SOA and Basic Software: HaloOS - Key Features
Vehicle OS: Latest Cooperation Dynamics
AI-Driven Software Development Transformation: HaloOS Simulator Tool
Software Development Tool Transformation: HaloOS Simulator Tool - Application Scenarios
5.2 NIO
SkyOS Full-domain AIOS Operating System: AI Model Engine - Framework
SkyOS Full-Domain AI Operating System: AI Model Engine - Key Components
SkyOS R&D History
SkyOS Architecture (1): Functional Characteristics of Components
SkyOS Architecture (2): SkyOS-M Core Based on seL4
SkyOS Architecture (2): SkyOS-M Development History and Challenges
SkyOS Architecture (3): SkyOS-R Performance Under Various Loads
SkyOS Architecture (4): Middleware
SkyOS Architecture (5): Data Closed-loop
How SkyOS Integrates AI and Enables Cockpit-Driving Integration
Application of AI Large Model Requires Computing Power Scheduling of Vehicle OS
SkyOS Application Cases: Cross-domain Invocation & Ultra-low Latency
SkyOS Application Cases: Aerial View System
SkyOS Application Cases: Valet Battery Swap (1)
SkyOS Application Cases: Valet Battery Swap (2)
SkyOS Application Cases: Valet Battery Swap (8)
SkyOS Application Cases: Data Security / 4D Comfort Pilot / High-spec Hardware
SkyOS and Cedar Digital Architecture (1)
SkyOS and Cedar Digital Architecture (2)
SkyOS and Cedar Digital Architecture (8)
Vehicle OS Scheduling Algorithms
Adaptable Chips
5.3 Xpeng
Vehicle OS Accelerates Integration
End-to-End Deterministic Vehicle OS
Vehicle SOA Communication Middleware
Vehicle-Cloud Integrated Vehicle SOA Middleware Platform
Hardware Sharing Cases under SOA
5.4 Xiaomi
Introduction to HyperOS
AIOS Development Direction
HyperOS Upgrade to 3.0
HyperOS Architecture Design (1)
HyperOS Architecture Design (2)
Hyper OS Architecture Design (5)
Vehicle OS Communication Technology under SOA
Open-source Vela
Technical Advantages of Vela
Vela Kernel Achieves ASIL-D Safety Level
Technical Advantages of Vela
Vela Ecosystem Cooperation
5.6 Leapmotor
Vehicle OS Architecture
Integrated Vehicle Architecture
Multi-Task Scheduling Model of Vehicle OS
5.6 Geely
Upgrade of AIOS
Full-Domain AI System
SOA-Based Operating System: GeelyOS
Intelligent Cockpit Solution: Flyme Auto IVI System
Meizu Flyme AI OS Allows for Integration with IVI
Advantages and Disadvantages of Flyme OS
Zeekr Intelligent Cockpit Solution: ZEEKR AI OS
Zeekr Vehicle OS Architecture
5.7 SAIC Motor
Design of Z-ONE AIOS
Full-Stack 4.0 AI Architecture: Middleware Framework AI OS
Full-Stack 4.0 AI Architecture: AI Application Layer Architecture
Full-Stack 4.0 AI Architecture: AI Application Layer Development - Agent Framework
Full-Stack 4.0 AI Architecture: AI Application Layer Development - Agent Application Ecosystem
Full-Stack 4.0 AI Architecture: Full-Domain Fusion Super Agent - IM Ultra Agent
5.8 Great Wall Motor
Cockpit OS: Coffee OS 3 Architecture
Features of AI OS
How Coffee OS Cooperates with Agent Scenarios
Vehicle OS
Cockpit Operating System: GC-OS
5.9 FAW Hongqi
Lingxi AI Cockpit OS Integrated with Agent
FAW.OS Architecture (1)
FAW.OS Architecture (2)
AIOS Integrated with Vehicle Large Model
Features of FAW.OS
5.10 GAC Group
Latest X-soul Architecture
Vehicle OS Architecture
Applications of Vehicle OS
5.11 Changan
Cockpit OS: Tops OS
RTDriveOS Architecture
Integrate AI into SOA Layer
SDA: RTDriveOS Intelligent Driving Operating System
SDA: L4 Layer - Operating System Layer
5.12 Dongfeng Motor
Vehicle OS Architecture
OS Development Process
Tianyuan OS
Tianyuan OS Architecture
Open-Source Components of Tianyuan Intelligent OS
Full-Domain Fusion of Tianyuan OS
EAI Agent Space of Tianyuan OS
Tianyuan Safe Vehicle Control OS
5.13 BYD
OS Architecture
Features of OS
Underlying Operating System Architecture of Intelligent Driving
5.14 Chery
Introduction to Chery OS
Application of Chery OS
Upgrade of Chery OS to AI OS

6 Operating Systems of Foreign OEMs

6.1 BMW
Evolution of BMW iDrive System
Agent Application Realized by BMW iDrive
Software-Defined Vehicle Architecture
6.2 Mercedes-Benz
Introduction to MB OS Functions
Architecture of MB OS
Latest Dynamics in Software Development Cooperation
6.3 Volkswagen
Introduction to VW.OS
Development History of VW.OS
Features of VW.OS
Architecture of VW.OS
6.4 Toyota
Introduction to Arene OS
Ecosystem Resources of Arene OS
Functions of Arene OS
Cooperation with NVIDIA on Operating System
6.5 Honda
ASIMO Operating System Derived from Robotics
ASIMO Operating System Has Learning Capabilities