Automotive Operating System and AIOS Integration Research Report, 2025

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Research on automotive AI operating system (AIOS): from AI application and AI-driven to AI-native

Automotive Operating System and AIOS Integration Research Report, 2025, released by ResearchInChina, explains the status quo and trends of AI application in automotive operating systems (OS), and analyzes how vehicle OS and AIOS mutually empower and co-evolve.

The relationship between vehicle OS and AIOS:
From 2023 to 2024, with the rise of central computing architecture, domain operating systems started evolving towards vehicle OS which takes on integrating the full-domain software system.
In the second half of 2024, AI foundation models started being mass-produced and introduced into vehicles, which raises new requirements for vehicle operating systems and also enables their scheduling capabilities, further facilitating the adoption of automotive AIOS.

AIOS is an AI-driven operating system that enables operating systems with "intelligence", that is, allow the systems to independently make optimizations and decisions during task execution and scheduling. AIOS represents the pinnacle of vehicle intelligence, and is responsible for handling complex perceptual data, executing intelligent decision, and realizing human-like interaction, while vehicle OS serves as the software foundation for all vehicle functions. The deep integration of the two is not merely a functional overlay but a key force driving reshaping of underlying architecture, deep synergy in industry chain, and redefinition of competitive rules.

1.Vehicle OS supports the implementation of AI capabilities: Beyond providing computing power and data, the SOA of vehicle OS abstracts vehicle functions into independent services through standardized interfaces, achieving hardware-software decoupling, and makes it easy to call interfaces across different software modules through atomic services, providing a stable and flexible invocation environment for AI models. Take Geely as an example:

Geely’s customized OS, GOS, is based on an SOA development framework that encapsulates various vehicle functions as services and allows AI functions to quickly call these services for agile development and iteration, providing the foundation for the rapid deployment and continuous optimization of AI capabilities. In early 2025, Geely introduced its "Full-Domain AI" system, and upgraded its OS to AIOS, with a model layer set up for AIOS to call.

2.AI reconstructs vehicle OS: Shifting it from the traditional "function-driven" model to a smarter "intent-driven" model:

AI Agents at the application layer can leverage foundation models' semantic analysis capabilities to accurately understand users' natural language commands and even latent intentions, and automatically invoke underlying software modules to complete tasks. The "intent-driven" interaction model is used to enable vehicles to proactively understand needs and provide services, making user experience much more natural and convenient.

Foundation models at the middleware (or model) layer not only provide calling interfaces for agents but also optimize the scheduling capabilities of vehicle OS through planning. This process relies on historical data and real-time system states, and uses reinforcement learning and operations research algorithms to dynamically allocate system resources and prioritize tasks. For instance, when a user simultaneously initiates navigation planning and high-definition video playback, foundation models can predict the urgency of route calculation and the resource demands of video decoding, coordinate CPU, GPU, and NPU compute in advance to ensure both navigation response and smooth video playback, avoiding stuttering caused by resource contention in traditional scheduling algorithms.

Data at the resource layer serves as the bridge between the two. Vehicle OS is responsible for data collection and management, while AIOS handles data analysis and decision-making.

In ArcherMind’s case, its subsidiary Arraymo developed ArraymoAIOS 1.0, an on-device AI operating system which, together with the vehicle operating system FusionOS 2.0, constitutes the technical base of AIOS. Key features of this base include:
Support use of Qualcomm SA8775P to build cockpit agents, and NVIDIA Orin to build vehicle agents, each equipped with 10+ deeply optimized on-device models (DeepSeek, Llama, Baichuan, Gemma, Yi-Chat, etc.).
Introduce intelligent scheduling algorithms to monitor and analyze multi-modal task loads (text, image, audio, etc.) in real time, and dynamically adjust the strategies for allocation of resources like CPU, GPU, and memory.
Introduce the AI acceleration engine AMLightning to efficiently schedule computing units in AI chips, allowing reasoning tasks to run on the most suitable computing unit.

AIOS 4.png

Evolution of AIOS: From AI Application and AI-Driven to AI-Native

In the automotive sector, AI was initially integrated at the application layer of the operating system, invoked via interfaces for specific scenarios. Entering the era of AIOS, AI starts penetrating deeper into the underlying layer, from being integrated into the middleware layer for driving functions, to touching the OS kernel and underlying architecture. In the future, it will evolve into AI-native OS.

As of April 2025, there have been three modes of AI integration in OS, corresponding to the three development phases of AIOS:
AI Application Phase: introduced as applications to serve scenarios.
AI-Driven Phase: connected at the middleware layer, utilizing components like AI Runtime and AI frameworks (models/agents/algorithm frameworks) to drive various software functions more flexibly.
AI-Native Phase: large language models (LLMs) are called as microkernel modular components, providing platform-level AI capabilities for the entire OS.

Huawei believes that the application of AI technology in terminal products typically passes through three phases: AI integration at the application layer, AI fusion at the system layer, and AI-centric new OS.

As of H1 2025, most OEMs have already deployed AI at the application layer and have begun to integrate AI components into the middleware layer. Examples include Li Auto’s Halo OS, NIO’s Sky OS, Xiaomi’s Hyper OS, and Geely’s AIOS GOS.

AI Application Phase

At this phase, AI is integrated into the application layer of OS to be called for scenarios. OS primarily provides computing power and data interfaces to optimize and upgrade basic AI functions like navigation and voice interaction. For example, in a "vehicle assistant" scenario, when a user calls AI for car-related knowledge, AI at the application layer first analyzes the request, converts it into a command, retrieves relevant data from databases, and formulates a natural-language answer displayed on the center console screen.

AI-Driven Phase

At this phase, AIOS extends into the middleware layer, becoming a mainstream approach for AI Agent invocation in intelligent cockpits. Upper-layer agents leverage AI components to directly call SOA atomic services via framework modules to control vehicle functions or other software features. Additionally, toolchains can be used to call multiple external tools and ecological interfaces to achieve "touchless" automation for scenarios.

For instance, the "people search by photographing" function of Li Auto’s MindVLA requires MindVLA to successively complete such steps as object recognition, map data matching, and route planning, involving use of components like AI reasoning framework and reasoning acceleration, and invocation of external maps and location data.

AIOS 9.png

Li Auto’s Halo OS incorporates an AI subsystem in the middleware layer, which includes not only AI Runtime but also components like AI reasoning engine and reasoning acceleration framework.

AI-Native Phase

AI-Native refers to systems or product forms that are fundamentally driven by AI, and deeply integrate AI in design from the ground up.

An AI-Native OS is an operating system that deeply integrates AI into its underlying architecture from the beginning of design, features system-level AI capabilities, and delivers all-scenario intelligent experience and rich agent ecosystems.

When AI and OS achieve deep integration, an AI-Native OS is formed. The system can intelligently optimize resource allocation and task scheduling according to application scenarios and demands, thus bringing a qualitative leap in overall efficiency and intelligence, rather than merely taking AI as an upper-layer application or functional module.

In Huawei’s case, its AI-Native OS has the following features:
Unified AI system base
AI-Native applications
Xiaoyi Super Agent
Open ecosystems

Underpinned by the AI system base, super apps/agents are built and rich ecosystems are created. AI-native HarmonyOS features multimodal understanding, personalized user data understanding, and privacy protection capabilities, and all-scenario perception and collaboration capabilities.

In April 2025, Huawei launched HarmonySpace 5, a HarmonyOS-based cockpit which adopts the MoLA hybrid foundation model architecture. It leverages a multi-model base (including DeepSeek), led by the PanGu Models, to enable system agent and vertical agent scenario applications. The entire upper-layer applications are supported by the system-level AI capabilities of HarmonyOS 5.0.

In ThunderSoft’s case, in 2025, AquaDrive OS has been upgraded to an AI-native OS, offering optimizations in the following directions:

The AI middleware of AquaDrive OS includes agent perception/execution services and an agent management framework to support multi-agent interaction. It also incorporates a foundation model inference and scheduling framework, supporting connection to various cloud and on-device foundation models to achieve life-oriented multimodal recognition and environmental guidance.

Its framework provides SOA services, and enables modular software function calls with atomized support.

Definitions

1 Status Quo and Trends of Automotive AIOS
1.1 Application Background of AIOS
Application Background of Vehicle OS in the AI Era
Requirements for Vehicle OS in the AI Era (1) - (3)
Overview of AI Application in Automotive OS

1.2 AIOS Architecture
Construction Methods of LLM OS
AIOS Architecture: Main Components and Functions of Kernel Module (1) - (7)
AIOS Architecture: Throughput and Latency/Performance Maintenance in Parallel State
AIOS Architecture: Agent Structure
AIOS Architecture: Model Deployment and Task Flow
AIOS Architecture: Definition and Characteristics of AI Runtime
AIOS Architecture: Comparison between Different AI Runtimes
AIOS Derived Framework: LSFS Improves File Management Efficiency
AIOS Derived Framework: Architecture of LSFS as an Additional Layer
AIOS Derived Framework: Implementation Modes of LSFS Functions

1.3 Cases and Insights of Terminal AIOS in Different Industries
Consumer-Grade AIOS Cases (1) - (2)
Enterprise-Grade AIOS Cases (1) - (2)
Insights from Terminal AIOS for Automotive AIOS

1.4 AIOS Trends
Trend 1: Vehicle OS Lays the Foundation for AIOS Implementation
Trend 2: AIOS Fusion Path
Trend 3:
Trend 4:
Trend 5: AI-Native OS and Cases

2 Overview of Automotive OS
2.1 Definition and History
Automotive Operating System (OS)
Evolution of Operating Systems
Vehicle OS: Definition
Vehicle OS: Evolution Process
Vehicle OS: Architecture
Vehicle OS: Characteristics
Vehicle OS: Development Models/Business Models
Summary of OEMs’ Vehicle OS (1) - (8)
Cross-Domain Scheduling of Vehicle OS: Algorithm Invocation

2.2 Trends of Automotive OS
Trend 1:
Trend 2:
Trend 3: Operating System Layout Modes of OEMs/Suppliers
Trend 4: OEMs’ Self-Developed Vehicle OS (1) - (9)

2.3 Classification of Automotive OS
Classification of Automotive OS: OS in Narrow/Broad Sense
Classification of Automotive OS: Real-Time OS and Non-Real-Time OS
List of Real-Time Operating System Suppliers and Their Products (1) - (3)
List of Non-Real-Time Operating System Suppliers and Their Products (1) - (2)
Classification of Automotive OS: Microkernel, Monolithic Kernel, Hybrid Kernel
Classification of Automotive OS: Vehicle Control and In-Vehicle OS
Automotive OS Market Size Forecast

2.4 Software Architecture
Software Architecture of Intelligent Vehicles
Software Ecosystem Framework of Intelligent Vehicles
Kernel Is the Core of Automotive Software Architecture

2.5 Business Models
Types of Business Models for Automotive OS
Business Models of Major Automotive OS Companies
Development Trends of Automotive OS and Business Model Exploration
Basic Automotive OS and Business Models
Automotive RTOS and Business Models (1)
Automotive RTOS and Business Models (2)
Operating Systems and Business Models of Suppliers (1) - (4)

2.6 Automotive Electronics Standard: AUTOSAR
Introduction to AUTOSAR
Classification of AUTOSAR
Key Members of AUTOSAR
Classic AUTOSAR: Architecture
Classic AUTOSAR: Functions
Adaptive AUTOSAR: Framework
Comparison Between Classic AUTOSAR and Adaptive AUTOSAR
Integration of Adaptive AUTOSAR and ROS
Core Points of AUTOSAR
Architecture of AUTOSAR China Working Group
Project Cases of AUTOSAR China Working Group
Business Models of AUTOSAR-Related Software Tool Suppliers (1) - (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 Automotive Electronics Standard: OSEK
Introduction to OSEK
Architecture and Characteristics of OSEK

2.8 Open Organization: COVESA
Introduction to COVESA
Members of COVESA
Key Achievements of COVESA
Example of COVESA Achievements
Primary Role of COVESA
Dynamics of COVESA

3 Basic Operating Systems
Introduction to Basic Automotive Operating Systems

3.1 BlackBerry
Development History of QNX in Automotive
QNX Business
QNX Products: Safety Levels
QNX Products: Features of Real-Time Operating System
QNX Products: Architecture of Real-Time Operating System
QNX Products: Cockpit Software Platform Solution (SDP 8.0)
QNX Products: Intelligent Assistance Platform
QNX Products: Cockpit-Driving Integration Controller
QNX Products: QNX Cloud Simulation Platform
QNX Products: Domain Controller Basic Software Platform
QNX OS for Safety: Product Panorama
QNX OS for Safety: Comparison of Safety Performance
Application of QNX in Robotics
QNX Partners
Dynamics of QNX

3.2 Linux & AGL
Members of AGL
Linux Architecture
RT-Linux
Open-Source Projects of Linux Foundation AI
AGL Application Framework: UCB

3.3 Android
Introduction to Android & Android Automotive OS
Android Automotive OS Architecture (1)
Android Automotive OS Architecture (2)
Features of Android Automotive OS
Android Auto Introduces AI Functions
Impacts of Slowed Updates of Android AOSP
User Development

3.4 Huawei
Introduction to HarmonyOS
Development History of HarmonyOS
Technical Architecture of HarmonyOS
Cooperation Models Between HarmonyOS and OEMs
Intelligent Driving Operating System: AOS
Intelligent Vehicle Control Operating System: VOS
Cross-Domain Integrated Software Framework: Vehicle Stack
iDVP Platform Upgrade
CCA
AI Functions of HarmonyOS
Two Implementation Modes of "Say and See" in HarmonyOS

3.5 Alibaba
Introduction to AliOS
Evolution Strategy of Banma Zhixing’s Vehicle OS
AliOS Architecture
Analysis of AliOS Application Layer
Integration of Alibaba’s Qianwen Model and OS: System Agent System
AliOS Solution: AliOS Intelligent Cockpit Operating System
AliOS Drive Intelligent Driving Operating System
Business Model of Banma Zhixing OS
Recent Dynamics of AliOS

3.6 VxWorks
Introduction to VxWorks
Wind River Software VxWorks Microkernel Architecture (1)
Wind River Software VxWorks Microkernel Architecture (2)
Wind River Products: Wind River Linux and Wind River AUTOSAR Adaptive Software Platform
Wind River Products: Helix Virtualization Platform
New Products of Wind River RTOS
Recent Dynamics in Automotive

3.7 Ubuntu
Introduction to Ubuntu
Applications of Ubuntu
Ubuntu’s Cooperation in Automotive

3.8 webOS
Development History of webOS
webOS OSE Components and Development Roadmap
webOS Can Be Integrated with AGL
Recent Dynamics in Automotive

3.9 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

4 Hypervisor
4.1 Introduction to Hypervisor
4.2 Comparison between Major Hypervisors
4.3 Status Quo of Hypervisor Industry
4.3 Status Quo of Hypervisor Industry: China
4.3 Status Quo of Hypervisor Industry: Global
4.4 Global Automotive Hypervisor Market Outlook
4.5 Business Models of Automotive Hypervisor Management System
4.5 Hypervisor Business Models (1) - (4)
4.6 QNX Hypervisor
Profile
Architecture
Solutions
4.7 ACRN
Profile
Components
4.8 COQOS Hypervisor
COQOS Hypervisor
COQOS Hypervisor SDK 9.5
Mixed VIRTIO / Non-VIRTIO Architecture
"Next Gen COQOS" Heterogeneous Cores
4.9 PikeOS
PikeOS
4.10 EB Corbos Hypervisor
EB Corbos Hypervisor
4.11 Harman Device Virtualization
Harman Device Virtualization
4.12 VOSYSmonitor
VOSYSmonitor
4.13 Zlingsmart
RAITE Hypervisor: System Design
RAITE Hypervisor: Intelligent Cockpit Solution

5 Generalized Automotive OSs and Companies
5.1 Neusoft Reach
Introduction to NeuSAR
Divide AIOS into Three Stages
Deployment of AI in 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 Completes DeepSeek Adaptation
NeuSAR aCore
Upgrades to AUTOSAR AP Products
NeuSAR cCore
Lightweight AUTOSAR CP Products
Collaboration with Infineon

5.2 ThunderSoft
AquaDrive OS Vehicle OS
Integration of Rubik Foundation Model with OS
AquaDrive OS Upgraded to AIOS
How AquaDrive OS Supports AI Function Implementation
How AquaDrive OS Supports AI Function Implementation: Cases

5.3 ArcherMind
Arraymo AIOS Base
Cross-Domain Vehicle OS: FusionOS 1.0
Cross-Domain Vehicle OS: FusionOS 2.0
Recent Dynamics

5.4 Kernelsoft
AI-Oriented Operating System Solutions
Real-Time Operating System
Linux
Operating System Security

5.5 Baidu
AI-Native Operating System: DuerOS X
AI-Native Operating System: Architecture
Integrated Vehicle OS Supply

5.6 iSOFT Infrastructure Software
AUTOSAR CP+AP Integrated Solutions (1)
AUTOSAR CP+AP Integrated Solutions (2)
CP Products
Vehicle OS Layout
Operating System Architecture
Vehicle Control OS: Open-Source EasyXMen
Intelligent Driving OS: EasyAda

5.7 ZTE GoldenOS
Microkernel and Macrokernel Technical Architecture
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
Intelligent Driving OS Solution: Chip Adaptation
Dynamics in Neusoft Reach + ZTE + SemiDrive Cooperation

5.8 AICC
Product System
ICVOS: Intelligent Connected Vehicle OS
ICVOS: Software Architecture
ICVOS: Development Architecture
ICVOS: SDK Architecture
ICVOS: Platform-Based, Connected, Scalable
ICVOS: Vehicle-Cloud Cooperation
ICVOS: Information Security Foundation Platform
ICVOS: New Architecture for Autonomous Driving Domain
ICVOS: Cases of Software Architecture Co-development with OEMs (1) - (4)

5.9 NVIDIA DRIVE OS
Introduction to DRIVE OS
DRIVE OS SDK Architecture

5.10 EB
Tresos Real-Time Operating System
Tresos AutoCore Architecture
EB’s J5-Based Intelligent Driving Domain OS
EB’s Virtualization Development Technology

5.11 Other OS Vendors
STEP’s Intelligent Driving OS Supports LLM and End-to-End Algorithm Deployment
iHUATEK Uses Large Vision Models to Build Vehicle OS
Freetech’s SOA Structure Is Connected to Foundation Models
Zlingsmart’s "RAITE OS" Microkernel OS
RT-Thread’s "Chenxuan" Vehicle Fusion Software Platform (RTOS)
Red Hat

6 Operating Systems of Chinese OEMs
6.1 Li Auto
Vehicle OS: Evolution
Vehicle OS: Architecture
Vehicle OS: Components and Features
Vehicle OS: Components (1) - Communication Middleware and Its Features
Vehicle OS: Components (2) - Vehicle Control OS and Its Features
Vehicle OS: Components (3) - Autonomous Driving OS and Its Features
Vehicle OS: Components (3) - Subsystems of Intelligent Driving OS
Vehicle OS: Components (4) - Virtualization Engine and Its Features
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
Halo OS Application Advantage 1:
Halo OS Application Advantage 2: Achieving Cross-Domain Scheduling
Halo OS Application Advantage 3:

6.2 NIO
Development History of SkyOS
SkyOS Architecture (1): Functional Features of Different Components
SkyOS Architecture (2): SkyOS-M Core Based On seL4
SkyOS Architecture (2): SkyOS-M Core Based On seL4
SkyOS Architecture (2): SkyOS-M Development History and Challenges
SkyOS Architecture (2): SkyOS-M Micro-Perception Self-Recovery Function
SkyOS Architecture (3): SkyOS-R Performance Under Different Loads
SkyOS Architecture (4): Middleware
SkyOS Architecture (4): Middleware
SkyOS Architecture (5): Data Closed Loop
How SkyOS Integrates AI and Achieves Cockpit-Driving Integration
Use of AI Foundation Models Requires Computing Power Scheduling of Vehicle OS
SkyOS Application Cases: Surround-View Display
SkyOS Application Cases: Valet Battery Swap Service (1)
SkyOS Application Cases: Valet Battery Swap Service (2)
SkyOS Application Cases: Valet Battery Swap Service (3)
SkyOS Application Cases: Valet Battery Swap Service (4)
SkyOS Application Cases: Valet Battery Swap Service (5)
SkyOS Application Cases: Valet Battery Swap Service (6)
SkyOS Application Cases: Valet Battery Swap Service (7)
SkyOS Application Cases: Valet Battery Swap Service (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 (3)
SkyOS and Cedar Digital Architecture (4)
SkyOS and Cedar Digital Architecture (5)
SkyOS and Cedar Digital Architecture (6)
SkyOS and Cedar Digital Architecture (7)
SkyOS and Cedar Digital Architecture (8)
Vehicle OS Scheduling Algorithm
Chip Adaptation

6.3 XPeng
Vehicle OS Integration Accelerates
Vision for Integration of OS and AI Foundation Model (1)
Vision for Integration of OS and AI Foundation Model (2)
Tianji AIOS

6.4 Xiaomi
AIOS-Driven Direction
Introduction to HyperOS and Its Development History (1)
Introduction to HyperOS and Its Development History (2)
HyperOS Architecture Design (1)
HyperOS Architecture Design (2)
HyperOS Architecture Design (3)
HyperOS Architecture Design (4)
HyperOS Architecture Design (5)
Vehicle OS Communication Technology Under SOA (1)
Vehicle OS Communication Technology Under SOA (2)
Vela Open Source
Vela Technical Advantages (1)
Vela Technical Advantages (2)
Vela Cooperation Ecosystem

6.5 Leapmotor
Vehicle OS Architecture
Vehicle Fusion Architecture
Vehicle OS Multi-Task Scheduling Method

6.6 Geely
Upgrade AIOS Operating System
Full-Domain AI System
SOA-Based OS: GeelyOS (1)
SOA-Based OS: GeelyOS (2)
SOA-Based OS: GeelyOS (3)
Intelligent Cockpit Solution: Flyme Auto IVI System
Meizu Flyme AI OS Can Integrate with IVI System
Advantages and Disadvantages of Flyme OS
Zeekr’s Intelligent Cockpit Solution: ZEEKR AI OS (1)
Zeekr’s Intelligent Cockpit Solution: ZEEKR AI OS (2)

6.7 SAIC
IM AIOS Enables Agent Implementation
IM AIOS Supports Multi-Agent Processes
Z-ONE’s AI Service Architecture
Z-ONE’s AI Service Architecture Is Built with 4 Layers
Z-ONE’s AIOS and Hardware Cooperation
Z-ONE’s AIOS Achieves Device-Cloud Integration Architecture (1)
Z-ONE’s AIOS Achieves Device-Cloud Integration Architecture (2)
Z-ONE’s Agent Cooperation Process

6.8 Great Wall Motor
Cockpit OS: Coffee OS 3 Architecture
Features of AI OS
How Coffee OS Coordinates Agent Scenarios
Vehicle OS and Central Computing Architecture (1)
Vehicle OS and Central Computing Architecture (2)

6.9 FAW
FAW.OS Architecture of FAW Hongqi (1)
FAW.OS Architecture of FAW Hongqi (2)
FAW AIOS Integrates Vehicle Foundation Models
Features of FAW.OS

6.10 GAC
Vehicle OS Architecture
Vehicle OS Application

6.11 Changan
Cockpit OS: Tianyu OS
RTDriveOS Architecture
Integrating AI into SOA Layer
SDA: RTDriveOS Intelligent Driving OS
SDA: L4 Layer – OS Layer
SDA: L4 Layer – OS Layer

6.12 Dongfeng
Vehicle OS Architecture (1)
Vehicle OS Architecture (2)
OS Development Process

6.13 BYD OS
BYD OS Architecture
BYD OS Features

6.14 Chery OS
Chery OS Introduction
Chery OS Application

6.15 BAIC’s AIOS Vision

7 Operating Systems of Foreign OEMs
7.1 From Customized Automotive OS To Vehicle OS
7.2 Comparison between Foreign Automotive OSs (1)
7.2 Comparison between Foreign Automotive OSs (2)
7.2 Comparison between Foreign Automotive OSs (3)
7.3 BMW
Mass-Production EEA: Software System Evolution
iDrive Enables Agent Application

7.4 Mercedes-Benz
MB OS Functions
MB OS Architecture (1)
MB OS Architecture (2)
MB OS Architecture (3)
MB OS Architecture (4)
Recent Development of MB OS

7.5 Volkswagen
Introduction to VW.OS
VW.OS Development History (1)
VW.OS Development History (2)
VW.OS Development History (3)
VW.OS Features (1)
VW.OS Features (2)
VW.OS Architecture

7.6 Toyota
Introduction to Arene OS (1)
Introduction to Arene OS (2)
Arene OS Ecosystem Resources
Arene OS Functions (1)
Arene OS Functions (2)
Cooperation With NVIDIA on OS

7.7 Honda
ASIMO Operating System

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